Question: Investigation into data mining models for application in the internet of things environment.

This document outlines a density-based algorithm designed for identifying clusters within extensive spatial databases that contain noise. The management of spatial databases necessitates the extraction of insights from voluminous data sets, with the ability to cope with irregularly shaped clusters. Clustering in the realm of data mining presents several requirements, including scalability, the handling of diverse attribute types, the discovery of clusters regardless of their shape, and minimal reliance on domain-specific knowledge for input parameter determination. The algorithm should also be capable of managing noise and outliers, remain indifferent to the sequence of input data, and effectively address high-dimensional data challenges. Additionally, the interpretability and usability of the discovered clusters are crucial for practical applications. In the context of the Internet of Things (IoT), where data mining models are pivotal, a robust clustering algorithm can significantly enhance the analysis of large-scale, spatially distributed IoT data, ensuring efficient and meaningful insights are derived even in the presence of noise and high data dimensionality.

In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.

Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.

The rapid growth of location-based social networks (LBSNs) has greatlyenriched people’s urban experience through social media and attracted increasingnumber of users in recent years. Typical location-based social networking sites allowusers to “check in” at a physical place and share the location with their onlinefriends, and therefore bridge the gap between the real world and online social networks.The purpose is that on the finalextraction of information, the results are worked for SocialRecommendation. However, the recommender systems presentsome failures in the filtering of the results and the way theyaresuggested to users.The method used here intendsto create an application thatperforms the Data Mining with textual information linked togeo-location data. The availability of large amounts of geographical and social data on LBSNsprovides an unprecedented opportunity to study human mobile behaviour throughdata analysis in a spatial-temporal-social context, enabling a variety of locationbasedservices, from mobile marketing to disaster relief.Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information information that can be used to increase revenue, cuts costs, or both.

From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.

Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need

Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy ecofeedback and demand side management research.

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details. 2010 Elsevier B.V. All rights reserved.

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.


Question: Chronovolumes: A direct rendering method for visualizing data that changes over time.

This paper presents an innovative method for the real-time decompression and visualization of animated volume data, which is essential for various applications but often hindered by the significant computational time and storage requirements. We introduce an algorithm that operates at interactive frame rates, utilizing 3D wavelet transforms. This technique is versatile, accommodating any wavelet, motion compensation methods, and a variety of encoding schemes for wavelet coefficients. We conduct a thorough analysis of different wavelet families and orders, evaluating their compression ratios and the error introduced. To enhance visual quality, we employ a quantization scheme optimized for the reconstructed volume's appearance, independent of the viewer's perspective. This approach facilitates high compression ratios while minimizing visual artifacts. To further boost compression, we implement a motion compensation scheme that leverages temporal coherence. With this system, we can decompress each volume in our animation at interactive frame rates, enabling visualization on a standard PC. Additionally, we showcase advanced visualization algorithms for high-quality display using OpenGL hardware, all running at interactive frame rates on conventional PCs.

On the issue of how to explore the complex relationships of entities in a story, Storyline visualization has proven to be a useful approach. The traditional Storyline visualization technology only applies to a linear continuous story and has the limitation of reflecting the development of the plots in a story. In this paper, we propose a hierarchical plot visualization method called StoryCake to improve this problem. First, according to the story elements, inter-sessions are divided into groups, whereby the layout of the entities will be optimized in individual groups. Then, the hierarchical relationships of entities in polar coordinates can be confirmed. Finally, we use several skills, e.g., interaction, labels and a fan-shaped visualization view, to enable people better understand how story evolves and track the hierarchical relationship more conveniently. Experimental results show that the proposed hierarchical plot visualization method in polar coordinates can reveal discontinuous events and nonlinear stories. The results also show that our algorithm presents the narrative structure and development of the story.

Wepresentarapidocclusioncullingalgorithmspecificallydesigned for urbanenvironments. For eachframe,an occlusionhorizonis beingbuilt on-the-flyduring a hierarchicalfront-to-backtraversal of the scene.All conservatively hiddenobjectsareculled, while all theoccludingimpostorsof all conservatively visibleobjectsare addedto the  D occlusionhorizon.Our framework alsosupports levels-of-detail(LOD) renderingby estimatingthe visible areaof the projectionof an objectin orderto selecttheappropriateLOD for eachobject.This algorithmrequiresno substantial preprocessing andnoexcessi ve storage. In a test sceneof 10,000buildings, the cull phasetook 11 ms on a PentiumII333 MHz and45 ms on an SGI Octaneper frame on average. In typical views, the occlusionhorizonculled away 80-90%of theobjectsthatwerewithin theview frustum,giving a 10 timesspeedupover view frustumculling alone.Combiningthe occlusionhorizonwith LOD renderinggavea17 timesspeedupon anSGIOctane,and23 timeson aPII. CR Categoriesand SubjectDescriptors: I.3.7[ComputerGraphics]: I.3.7 Three-Dimensional GraphicsandRealism.

Time series visualization of streaming telemetry (i.e., charting of key metrics such as server load over time) is increasingly prevalent in modern data platforms and applications. However, many existing systems simply plot the raw data streams as they arrive, often obscuring large-scale trends due to small-scale noise. We propose an alternative: to better prioritize end users’ attention, smooth time series visualizations as much as possible to remove noise, while retaining large-scale structure to highlight significant deviations. We develop a new analytics operator called ASAP that automatically smooths streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis). We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning, pixel-aware preaggregation, and ondemand refresh. We demonstrate that ASAP can improve users’ accuracy in identifying long-term deviations in time series by up to 38.4% while reducing response times by up to 44.3%. Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de .

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.

We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.

We propose a new approach to visualize social networks. Most common network visualizations rely on graph drawing. While without doubt useful, graphs suffer from limitations like cluttering and important patterns may not be realized especially when networks change over time. Our approach adapts pixel-oriented visualization techniques to social networks as an addition to traditional graph visualizations. The visualization is exemplified using social networks based on corporate wikis.

Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.

Although luminance contrast plays a predominant role in motion perception, significant additional effects are introduced by chromatic contrasts. In this paper, relevant results from psychophysical and physiological research are described to clarify the role of color in motion detection. Interpreting these psychophysical experiments, we propose guidelines for the design of animated visualizations, and a calibration procedure that improves the reliability of visual motion representation. The guidelines are applied to examples from texture-based flow visualization, as well as graph and tree visualization.

Selecting the appropriate visual presentation of the data such that it not only preserves the semantics but also provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our DataVizard system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy.


Question: Chronovolumes: A direct rendering method for visualizing data that changes over time.

This paper explores the utilization of multi-dimensional transfer functions and direct manipulation widgets for interactive volume rendering, aiming to enhance the visualization of complex data. Traditionally, direct volume renderings rely on one-dimensional transfer functions, which map color and opacity based solely on the scalar values within the dataset. However, multi-dimensional transfer functions offer a more nuanced approach, capable of highlighting specific material boundaries and conveying intricate surface properties. These functions, though more sophisticated, are less common due to the challenge in defining appropriate mappings in dimensions beyond one. The paper highlights a significant category of three-dimensional transfer functions for scalar datasets, which incorporate data value, gradient magnitude, and a secondary directional derivative. To facilitate the specification of such complex transfer functions, the authors introduce a set of direct manipulation widgets, designed to make the process intuitive and user-friendly. These widgets enable users to interactively adjust transfer function parameters, providing a more engaging and insightful exploration of the volume data. Moreover, the paper discusses the integration of modern graphics hardware to support real-time rendering with multi-dimensional transfer functions, creating a robust system for interactive volume data exploration. This combination of advanced transfer functions, user-friendly widgets, and hardware-accelerated rendering empowers users to effectively analyze and interpret time-varying data, enhancing the visual representation of complex datasets.

We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

Statistical analysis of functional magnetic resonance imaging (fMRI), such as independent components analysis, is providing new scientific and clinical insights into the data with capabilities such as characterising traits of schizophrenia. However, with existing approaches to fMRI analysis, there are a number of challenges that prevent it from being fully utilised, including understanding exactly what a 'significant activity' pattern is, which structures are consistent and different between individuals and across the population, and how to deal with imaging artifacts such as noise. Interactive visual analytics has been presented as a step towards solving these challenges by presenting the data to users in a way that illuminates meaning. This includes using circular layouts that represent network connectivity and volume renderings with 'in situ' network diagrams. These visualisations currently rely on traditional 2D 'flat' displays with mouse-and-keyboard input. Due to the constrained screen space and an implied concept of depth, they are limited in presenting a meaningful, uncluttered abstraction of the data without compromising on preserving anatomic context. In this paper, we present our ongoing research on fMRI visualisation and discuss the potential for virtual reality (VR) and augmented reality (AR), coupled with gesture-based inputs to create an immersive environment for visualising fMRI data. We suggest that VR/AR can potentially overcome the identified challenges by allowing for a reduction in visual clutter and by allowing users to navigate the data abstractions in a 'natural' way that lets them keep their focus on the visualisations. We present exploratory research we have performed in creating immersive VR environments for fMRI data.

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.

Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We conducted two quantitative user studies to evaluate different visual representations for such dense many-to-many flows. In our first study we compared a bundled node-link flow map representation and OD Maps [37] with a new visualisation we call MapTrix. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar.

Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.

A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples. In recent years a number of methods have been devised to compute dynamical parameters from time series [ 11. Such parameters are the information dimension, entropy, Liapunov exponents, dimension spectrum, etc. In all cases it is assumed that the time series is obtained from an autonomous dynamical system, i .e. the evolution equations do not contain the time explicitly. It is also assumed that the time series is much longer than the characteristic times of the dynamical system. In the present letter we present a new diagnostic tool which we call recurrence plot; this tool tests the above assumptions, and gives useful information also when they are not satisfied. As the examples will show, the information obtained from recurrence plots is often surprising, and not easily obtainable by other methods. Let x( i ) be the i-th point on the orbit describing a dynamical system in d-dimensional space, for i = 1, ..., N . The recurrence plot is an array of dots in a N x N square, where a dot is placed at (i, j) whenever x( j) is sufficiently close to x( i ) . In practice one proceeds as follows to obtain a recurrence plot from a time series {ui}. First, choosing an embedding dimension d, one constructs the d-dimensional orbit of X(i) by the method of time delays (i.e. if the ui are scalar, x(i) = (ui, u ~ + ~ , ..., u ~ + ~ ~ ) ) . Next, one chooses r(i) such that the ball of radius r(i) centred at x (i) in Rd contains a reasonable number of other points x (j) of the orbit (in the examples below, we have chosen a sequence of increasing radii and stopped when we found at least 10 neighbours). In our computation we have allowed the radius r(i) to depend on the point x ( i ) , and r(i) has been selected by a routine used in our alghorithm for the determination of the Liapunov exponents-see ref. [2]. Finally, one plots a dot at each point (i, j) for which x ( j ) is in the ball of radius r(i) centred at x( i ) . We call this picture a recurrence plot. Note that the i, j are in fact times: therefore a recurrence plot describes natural (but subtle) time correlation information. Recurrence plots tend to be fairly symmetric with respect to the diagonal i =j because if x ( i ) is close to x(j), then x ( j ) is close to x( i ) . There is, however, no complete symmetry because we do not require r(i) = r ( j ) . 974 EUROPHYSICS LETTERS Fig. 1. A scatter plot for 20000 iterates of the Henon map (x, y)+ (1 1.4x2+ y, 0 . 3 ~ ) . The embedding dimension is 8. In this figure, and in figs. 2 and 4, the diagonal is added. We shall now analyse some recurrence plots obtained from experimental or computergenerated time series. We distinguish-somewhat arbitrarily-between large-scale typology and small-scale textuye. For an autonomous dynamical system, if all the characteristic times are short compared to the length of the time series, the typology of the recurrence plot is homogeneous. This means that the overall pattern is uniformly grey although at small scale nontrivial texture may be visible (see below). An example of a homogeneous recurrence plot is provided by the Henon map, as exhibited in fig. 1. Another typology is provided by the time evolution with drif t , i .e. by dynamical systems which are not quite homogeneous, but contain adiabatically (slowly) varying parameters [3]. To get an example we started with a time series obtained from the Lorenz system, and added a term varying linearly with time (from 0 to 10 percent of the amplitude of the original signal). The corresponding recurrence plot is our fig. 2. The rich structure of this plot is due to the Lorenz system itself and not to the drift; the main effect of the drift is to make the plot somewhat paler away from the diagonal and darker near it (progressive decorrelation at large time intervals). Since the human eyehrain is not very good at seeing small variations of overall darkness, a histogram of darkness as a function of i j is given in fig. 3; this shows unquestionably the existence of a drift. Our next recurrence plot, fig. 4, shows what might be called a periodic typology; it is obtained from an experimental time series communicated by Ciliberto and described in ref. [21. This striking picture shows that-after a transient-the system goes into slow oscillations superimposed on the chaotic motion which is otherwise known to exist (2 positive characteristic exponents have been found in ref[2]). These slow oscillations (which are not quite periodic) are not easily recognizable on the original time series (and certainly have gone unrecognized in the original analysis). J.-P. ECKMANN et al.: RECURRENCE PLOTS OF DYNAMICAL SYSTEMS 975

This paper introduces particle systems--a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems model an object as a cloud of primitive particles that define its volume. Over a period of time, particles are generated into the system, move and change form within the system, and die from the system. The resulting model is able to represent motion, changes of form, and dynamics that are not possible with classical surface-based representations. The particles can easily be motion blurred, and therefore do not exhibit temporal aliasing or strobing. Stochastic processes are used to generate and control the many particles within a particle system. The application of particle systems to the wall of fire element from the Genesis Demo sequence of the film Star Trek II: The Wrath of Khan [10] is presented.

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we find that all designs are assembled from the building blocks of juxtaposition, superposition, and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.


Question: Chronovolumes: A direct rendering method for visualizing data that changes over time.

This paper presents a novel technique for the construction of orthonormal bases composed of compactly supported wavelets, featuring customizable levels of smoothness. The regularity of these wavelets increases in direct proportion to the width of their support. Initially, we delve into the foundational theory of multiresolution analysis and explore various algorithms pertinent to vision decomposition and reconstruction. Our method integrates and refines these diverse approaches to achieve the desired construction of wavelet bases. This synthesis not only ensures high regularity but also compact support, making the wavelets ideal for a multitude of applications requiring precise control over both smoothness and spatial localization.

We present a real-time stereoscopic video-see-through augmented reality (AR) system applied to the medical procedure known as ultrasound-guided needle biopsy of the breast. The AR system was used by a physician during procedures on breast models and during non-invasive examinations of human subjects. The system merges rendered live ultrasound data and geometric elements with stereo images of the patient acquired through head-mounted video cameras and presents these merged images to the physician in a head-mounted display. The physician sees a volume visualization of the ultrasound data directly under the ultrasound probe, properly registered within the patient and with the biopsy needle. Using this system, a physician successfully guided a needle into an artificial tumor within a training phantom of a human breast. We discuss the construction of the AR system and the issues and decisions which led to the system architecture and the design of the video see-through head-mounted display. We designed methods to properly resolve occlusion of the real and synthetic image elements. We developed techniques for realtime volume visualization of timeand position-varying ultrasound data. We devised a hybrid tracking system which achieves improved registration of synthetic and real imagery and we improved on previous techniques for calibration of a magnetic tracker. CR

A two-handed direct manipulation VE (virtual environment) interface has been developed as an intuitive manipulation metaphor for graphical objects. A new input device called ChordGloves introduces a simple technique for rapid and repeatable gesture recognition; the Chordgloves emulate a pair of 3-D mice and a keyboard. A drafting table is isomorphically mapped into the VE and provides hand support for 2-D interface techniques, as well as a reference frame for calibrating the mapping between real and virtual worlds. A cursor gravity function is used to grab vertices, edges, or faces and establish precisely aligned differential constraints between objects called anchors. The capability of subjects to translate, rotate, scale, align, and glue objects is tested with a puzzle building task. An approximation of the puzzle task is done in Adobe Illustrator to provide a performance reference. Results and informal user observations as well as topics for future work are presented.

Due to the technological progress over the last decades, today’s scientific and commercial applications are capable of generating, storing, and processing, massive amounts of data sets. This influences the type of data generated, which in turn means that with each data entry di erent aspects are combined and stored into one common database. Often the describing attributes are numeric; we name data with more than a handful attributes (dimensions) high-dimensional. Having to make use of these types of data archives provides new challenges to analysis techniques. The work of this thesis centers around the question of finding interesting patterns (meaningful information) in high-dimensional data sets. This task is highly challenging because of the so called curse of dimensionality, expressing that when dimensionality increases the data becomes sparse. This phenomena disturbs standard analysis techniques. Automatic techniques have to deal with the data complexity not only increasing their runtime, but also vitiating their computation functions (like distance functions). Moreover, exploring these data sets visually is hindered by the high number of dimensions that have to be displayed on the two dimensional screen space. This thesis is motivated by the idea that searching for interesting patterns in this kind of data can be done through a mixed approach of automation, visualization, and interaction. The amount of patterns a visualization contains can be measured by so called quality metrics. These automated functions can then filter the high number of highdimensional visualizations and present to the user a pre-filtered good subset for further investigation. We propose quality metrics for scatterplots and parallel coordinates focusing on di erent user tasks like identifying clusters and correlations. We also evaluate these measures with regard to (1) their ability to identify clusters in a variety of real and synthetic datasets; (2) their correlation with human perception of clusters in scatterplots. A thorough discussion of results follows reflecting the impact on directions for future research. As quality metrics were developed for a large number of di erent high-dimensional visualization techniques, we present our reflections on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a comprehensive literature review. In high-dimensional data, patterns exist often only in a subset of the dimensions. Subspace clustering techniques aim at finding these subspaces where clusters exist and which might otherwise be hidden if a traditional clustering algorithm is applied. While subspace clustering approaches tackle the sparsity problem in high-dimensional data well, designing e ective visualization to help analyzing the clustering result is not trivial. In addition to the cluster membership information, the relevant sets of dimensions and the overlaps of memberships and dimensions need to also be considered. Although, a number of techniques (for example, scatterplots, heat maps, dendrograms, hierarchical parallel coordinates) exist for visualizing traditional clustering results, little research has been done for visualizing subspace clustering results. Moreover, while extensive research has been carried out with regard to designing subspace clustering algorithms, surprisingly little attention has been paid to the developing of e ective visualization tools analyzing the viii clustering result. Appropriate visualization techniques will not only help in monitoring the clustering process but, with special mining techniques, they could also enable the domain expert to guide and even to steer the subspace clustering process to reveal the patterns of interest. To this goal, we envision a concept that combines subspace clustering algorithms and interactive scalable visual exploration techniques. This work includes the task of comparative visualization and feedback guided computation of alternative clusterings. Zusammenfassung Bedingt durch den technologischen Fortschritt der letzten Jahrzehnte sind heutige kommerzielle Applikationen in der Lage, riesige Datenmengen zu erzeugen, zu speichern und zu verarbeiten. Diese Entwicklung beeinflusst auch die Natur der erzeugten Daten, d.h. dass für jeden Dateneintrag unterschiedliche Aspekte in der gleichen Datenbank gespeichert werden. Oft sind die beschreibenden Attribute numerisch. Datensätze, die mehr als fünf solcher Attribute (Dimensionen) beinhalten, nenne ich hochdimensional. Der wertbringende Gebrauch solcher Datenarchive bringt neue Herausforderungen an Analysetechniken mit sich. Die vorliegende Dissertation bearbeitet die Fragestellung, wie interessante Muster (bedeutende Information) in hochdimensionalen Räumen gefunden werden können. Diese Aufgabenstellung ist durch das Problem des Fluches der Dimensionalität äußerst herausfordernd. Dieses Problem besagt, dass Daten im hochdimensionalen Raum spärlich vorkommen. Herkömmliche Analysetechniken werden dadurch beeinträchtigt. Automatische Methoden müssen die Datenkomplexität nicht nur ihre Laufzeit, sondern auch ihre Berechnungsfunktionen (z.B. Distanzfunktionen) betre end, einbeziehen. Außerdem wird die visuelle Exploration dieser Daten durch die Zweidimensionalität der Darstellungen beeinträchtigt. Diese Dissertation stützt sich auf das Konzept, dass die Suche nach interessanten Mustern in hochdimensionalen Datenmengen mit einem kombinierten Ansatz von automatischen, visuellen und interaktiven Methoden durchgeführt werden kann. Die Ausprägung der Muster einer Visualisierung kann durch sogenannte Qualitätsmaße gemessen werden. Durch diese automatischen Funktionen kann die große Menge an hochdimensionalen Visualisierungen eingegrenzt und dem Benutzer eine ausgewählte Menge zur weiteren Untersuchung zur Verfügung gestellt werden. Ich schlage Qualitätsmaße für Scatterplots und Parallele Koordinaten vor, die sich auf unterschiedliche Aufgaben, wie die Identifikation von Gruppen oder Korrelationen, konzentrieren. Zusätzlich werden diese Techniken bezüglich (1) ihrer Fähigkeit Cluster in unterschiedlichen realen und synthetischen Datensätzen und (2) ihrer Korrelation mit der menschlichen Wahrnehmung untersucht. Der ausführlichen Diskussion dieser Resultate folgen Überlegungen für die zukünftige Forschung. Da viele verschiedene Qualitätsmaße für eine Reihe weiterer hochdimensionaler Visualisierungen entwickelt wurden, werde ich Vorschläge für deren Vernetzung und Weiterentwicklung vorstellen. Hierfür wird eine Übersicht über die verschiedenen Ansätze erstellt, welcher eine Systematisierung zugrunde liegt, die aufgrund einer umfassenden Literaturauswertung zustande kam. Im hochdimensionalen Raum existieren manche Muster nur in verschiedenen Unterräumen des Datenraumes. Subspace Clustering Algorithmen wurden entwickelt, um Unterräume zu finden in denen Cluster existieren, die durch traditionelle Clustering Algorithmen nicht gefunden werden würden. Obwohl diese Algorithmen spärlich mit Daten besetzte, hochdimensionale Räume gut explorieren können, ist das Entwickeln von e ektiven Visualisierungstechniken, um diese Clusteringresultate zu analysieren, nicht trivial. Zusätzlich zu der Clusterzugehörigkeit von Elementen müssen die relevanten Attributmengen eines Clusters und die Objektund Dimensionsüberlappungen von Subspaceclusx tern dargestellt werden. Auch wenn eine Reihe von Techniken für die Visualisierung von traditionellen Clustering Resultaten existiert (z.B. Scatterplots, Heatmaps, Dendrogramme, hierarchische Parallele Koordinaten) gibt es nur wenige Ansätze, um das Resultat von Subspace Clustering Algorithmen zu visualisieren. Außerdem wurden bisher erstaunlich wenige Ansätze vorgestellt, die eine visuelle Analyse der Subspace Clustering Ergebnisse unterstützen können, obwohl im Bereich der Subspace Clustering Algorithmen viel Forschung betrieben wurde. Angemessene Visualisierungstechniken, die von speziellen Methoden zur Extraktion von Informationen unterstützt werden, würden nicht nur die Nachverfolgung der Clustering Ergebnisse ermöglichen, sondern auch Fachleuten dabei helfen, den Subspace Clustering Prozess so zu steuern, dass relevante Muster zum Vorschein kommen. Dieses Ziel vor Augen stelle ich ein Konzept vor, das Subspace Clustering Algorithmen mit interaktiven skalierbaren Visualisierungen kombiniert. Meine Ansätze widmen sich deshalb der Aufgabe der Visualisierung zum Vergleich von alternativen Clustergruppen, die durch Nutzerfeedback gesteuert werden.

This paper presents a new shading model for real-time rendering of plant leaves that reproduces all important attributes of a leaf and allows for a large number of leaves to be shaded. In particular, we use a physically based model for accurate subsurface scattering on the translucent side of directly lit leaves. For real-time rendering of this model, we formulate it as an image convolution process and express the result in an efficient directional basis that is fast to evaluate. We also propose a data acquisition method for leaves that uses off-the-shelf devices.

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.

Wepresentarapidocclusioncullingalgorithmspecificallydesigned for urbanenvironments. For eachframe,an occlusionhorizonis beingbuilt on-the-flyduring a hierarchicalfront-to-backtraversal of the scene.All conservatively hiddenobjectsareculled, while all theoccludingimpostorsof all conservatively visibleobjectsare addedto the  D occlusionhorizon.Our framework alsosupports levels-of-detail(LOD) renderingby estimatingthe visible areaof the projectionof an objectin orderto selecttheappropriateLOD for eachobject.This algorithmrequiresno substantial preprocessing andnoexcessi ve storage. In a test sceneof 10,000buildings, the cull phasetook 11 ms on a PentiumII333 MHz and45 ms on an SGI Octaneper frame on average. In typical views, the occlusionhorizonculled away 80-90%of theobjectsthatwerewithin theview frustum,giving a 10 timesspeedupover view frustumculling alone.Combiningthe occlusionhorizonwith LOD renderinggavea17 timesspeedupon anSGIOctane,and23 timeson aPII. CR Categoriesand SubjectDescriptors: I.3.7[ComputerGraphics]: I.3.7 Three-Dimensional GraphicsandRealism.

For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.

Image-based rendering techniques include those with geometry and those without. Geometric information in the form of a depth map aligned with the image holds a lot of promise for IBR due to the several methods available to capture it. It can improve the quality of generated views using a limited number of views. Compression of light fields or multiple images has attracted a lot of attention in the past. Compression of multiple depth maps of the same scene has not been explored much in the literature. We propose a method for compressing multiple depth maps in this paper using geometric proxy. Different quality of rendering and compression ratio can be achieved by varying different parameters. Experiments show the effectiveness of the compression technique on several model data.

Simulation and modeling of turbulent flow, and of turbulent reacting flow in particular, involve solving for and analyzing time-dependent and spatially dense tensor quantities, such as turbulent stress tensors. The interactive visual exploration of these tensor quantities can effectively steer the computational modeling of combustion systems. In this article, the authors analyze the challenges in dense symmetric-tensor visualization as applied to turbulent combustion calculation; most notable among these challenges are the dataset size and density. They analyze, together with domain experts, the feasibility of using several established tensor visualization techniques in this application domain. They further examine and propose visual descriptors for volume rendering of the data. Of these novel descriptors, one is a density-gradient descriptor which results in Schlieren-style images, and another one is a classification descriptor inspired by machine-learning techniques. The result is a hybrid visual analysis tool to be utilized in the debugging, benchmarking and verification of models and solutions in turbulent combustion. The authors demonstrate this analysis tool on two example configurations, report feedback from combustion researchers, and summarize the design lessons learned. c © 2016 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci.Technol.2016.60.1.010404] INTRODUCTION Computational simulation of turbulent combustion for gas turbine design has become increasingly important in the last two decades, due in part to environmental concerns and regulations on toxic emissions. Such modern gas turbine designs feature a variety of mixing fuel compositions and possible flow configurations,1,2 which make non-computational simulations difficult. The focus of the computational research effort in this direction is on the development of computational tools for the modeling and prediction of turbulent combustion flows. Received June 30, 2015; accepted for publication Nov. 4, 2015; published online Dec. 10, 2015. Associate Editor: Song Zhang. 1062-3701/2016/60(1)/010404/11/$25.00 Tensor quantities are common features in these turbulent combustion models. In particular, stress and strain tensors are often correlated to turbulent quantities—which appear unclosed in the mathematical formulation and thus need to be modeled as part of the computational simulation. Visual identification of the characteristics of such tensor quantities can bring significant insights into the computational modeling process. However, these computational tensor fields are very large and spatially dense—a good example of the Big Data revolution across sciences and engineering. Figure 1 shows an example turbulent combustion configuration, featuring a grid size of 106 and 6× 106 particles (shown as spheres); this dataset should be considered in contrast to traditional tensor datasets, which feature grid sizes in the 102 range. At such large scales, typical glyph encodings become cluttered and illegible. Furthermore, combustion experts seldom have an intuitive understanding of the tensor quantities. In this respect, froma tensor visualization perspective, workingwith these datasets poses an array of challenges. Are traditional tensor and flow representations useful in this context? Does increasing the level of complexity or expressiveness of such representations help or hinder? Is interaction speed more important than the benefits gained from complex descriptors? In this article, we address a specific application design problem. In the process of exploring the design space, we also investigate some of the larger visualization questions above, through the opportunity of a case study in the computational-combustion domain. In thiswork,motivated by an ongoing collaborationwith domain experts,3 we investigate the challenges associated with the exploratory visualization of tensor quantities in turbulent combustion simulations. We first provide a characterization of the problem domain, including a data analysis. Through a case study involving five senior combustion researchers, we then iteratively explore the space of tensor visual encodings. We implement and evaluate several J. Imaging Sci. Technol. 010404-1 Jan.-Feb. 2016 Marai et al.: Visual descriptors for dense tensor fields in computational turbulent combustion: a case study Figure 1. One timestep in an example turbulent combustion configuration. The grid size is 106. In this image, 6× 106 particles are shown as spheres. This dataset should be contrasted to traditional tensor datasets, which feature sparse grids in the 102 range. At this scale, typical glyph encodings become cluttered. approaches advocated by the visualization community in an interactive prototype, and we contrast these approaches with the best-of-breed visualization practices in the target domain. Based on domain expert feedback, we then focus our efforts on identifying effective visual descriptors for volume rendering of the combustion tensor data. Our contributions include a novel density-gradient descriptor and the adaptation of a machine-learning classification technique. Next, we evaluate the visual descriptors on two computational-combustion datasets of particular interest, and we show the importance of the proposed approach for debugging the numerical simulation of complex configurations. In an effort to better bridge the gap between the combustion and tensor visualization communities, we describe these tensor field datasets. Last but not least, we contribute a summary of design lessons learned from the study and from the application design process. To the best of our knowledge, this is the first formal, exploratory case study of tensor visualization techniques in the context of very large, high-density turbulent combustion flow. TENSORS IN TURBULENT COMBUSTION MODELING Turbulent Combustion Modeling. A sufficiently accurate, flexible and reliable model can be used for an in silico combustor rig test as a much cheaper alternative to the reallife rig tests employed in combustor design and optimization. In order to achieve such amodel, themethodology should be well tested and proven with lab-scale configurations. Multiple numerical approaches exist for the generation of such computational models of combustion, most notably Direct numerical simulation (DNS), Reynolds-averaged Navier–Stokes (RANS) and Large eddy simulation (LES). DNS, RANS and LES have complementary strengths. However, allmodels begin by describing the compressible reacting flow via a set of partial differential equations (PDEs) that represent the conservation of mass, momentum and energy. These PDEs are a fully coupled set of multi-dimensional non-linear equations and can be posed in a variety of forms depending on the flow conditions (compressibility, scale, flow regime, etc.).4 In this article, we exemplify the visualization of stress/strain tensors, and therefore restrict the presentation to the pertinent subset of these PDEs, namely the momentum transport equation. Stress, Strain and Turbulent Stress Tensors. A tensor is an extension of the concept of a scalar and a vector to higher orders. For example, while a stress vector is the force acting on a given unit surface, a stress tensor is defined as the components of stress vectors acting on each coordinate surface; thus, stress can be described by a symmetric second-order tensor (a matrix). The velocity stress and strain tensor fields aremanifested in the transport of fluid momentum, which is a vector quantity governed by the following conservation equation:

The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.


Question: Chronovolumes: A direct rendering method for visualizing data that changes over time.

This paper explores the innovative application of wavelet theory and its practical implications in the realm of computer graphics, focusing on both theoretical foundations and real-world applications. For an in-depth exploration of the subject, including detailed explanations and extensive case studies, readers are encouraged to consult the comprehensive book available through MorganKaufmann or Amazon.com. This resource delves into the intricacies of wavelets, their computational efficiency, and their utility in enhancing visual representation in computer graphics. From the theoretical underpinnings to the practical implementation, the book offers a detailed guide, making it an invaluable resource for researchers, practitioners, and enthusiasts interested in leveraging wavelet techniques for advanced graphics rendering and analysis.

The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.

The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.

Clouds are an important aspect of rendering outdoor scenes. Thi paper describes a cloud system that extends texture splatting on par ticles to model a dozen cloud types (e.g., stratus, cumulus congestus, cumulonimb us), an improvement over earlier systems that modeled only one type of cumulus. W e also achieve fast real-time rendering, even for scenes of dense overcast cove rage, which was a limitation for previous systems. We present a new shading model that uses artist-driven contr ols rather than a programmatic approach to approximate lighting. This is sui table when fine-grained control over the look-and-feel is necessary, and artistic r esources are available. We also introduce a way to simulate cloud formation and dissipa tion using texture

A novel algorithm is presented to compute the convex hull of a point set in ℝ3 using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The other extreme vertices of the convex hull are then found by using a two-round checking in the digital and the continuous space successively. The algorithm does not need explicit locking or any other concurrency control mechanism, thus it can maximize the parallelism available on the modern GPU.
 The implementation using the CUDA programming model on NVIDIA GPUs is exact and efficient. The experiments show that it is up to an order of magnitude faster than other sequential convex hull implementations running on the CPU for inputs of millions of points. The works demonstrate that the GPU can be used to solve nontrivial computational geometry problems with significant performance benefit.

This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.

We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.

Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-users the layout and formatting already present in the spreadsheet.

A rendering is an abstraction that favors, preserves, or even emphasizes some qualities while sacrificing, suppressing, or omitting other characteristics that are not the focus of attention. Most computer graphics rendering activities have been concerned with photorealism, i.e., trying to emulate an image that looks like a highquality photograph. This laudable goal is useful and appropriate in many applications, but not in technical illustration where elucidation of structure and technical information is the preeminent motivation. This calls for a different kind of abstraction in which technical communication is central, but art and appearance are still essential instruments toward this end. Work that has been done on computer generated technical illustrations has focused on static images, and has not included all of the techniques used to hand draw technical illustrations. A paradigm for the display of technical illustrations in a dynamic environment is presented. This display environment includes all of the benefits of computer generated technical illustrations, such as a clearer picture of shape, structure, and material composition than traditional computer graphics methods. It also includes the three-dimensional interactive strength of modem display systems. This is accomplished by using new algorithms for real time drawing of silhouette curves, algorithms which solve a number of the problems inherent in previous methods. We incorporate current non-photorealistic lighting methods, and augment them with new shadowing algorithms based on accepted techniques used by artists and studies carried out in human perception. This paper, all of the images, and a mpeg video clip are available at http://www.cs.utah.edu/~bgooch/ITI CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.

We conducted human study to provide reference to our current CD and EMD values reported on the rendered dataset. We provided the human subject with a GUI tool to create a triangular mesh from the image. The tool (see Fig 1) enables the user to edit the mesh in 3D and to align the modeled object back to the input image. In total 16 models are created from the input images of our validation set. N = 1024 points are sampled from each model.


Question: Precision and detail of Kinect depth information for indoor mapping purposes.

KinectFusion: Real-time 3D Reconstruction and Interactive Capabilities Using a Portable Depth Camera

KinectFusion revolutionizes the process of creating detailed 3D reconstructions of indoor environments by utilizing a standard Kinect camera in real-time. This system solely depends on the depth data from the Kinect to accurately track the 3D position of the camera and construct geometrically precise 3D models of the physical scene as the user moves through the space. The paper provides a comprehensive overview of the innovative GPU-based pipeline that powers KinectFusion, showcasing its applications in low-cost handheld 3D scanning, geometry-aware augmented reality, and physics-based interactions.

Moreover, KinectFusion includes advanced features that allow for object segmentation and user interaction directly in front of the camera, without compromising the quality of camera tracking or the reconstruction process. These enhancements enable real-time multi-touch interactions on any surface, be it planar or non-planar, by seamlessly integrating the physical reconstructed surfaces for touch input. This capability opens up new possibilities for interactive experiences in a variety of settings.

Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter.

Most existing approaches to characterizing thermal properties of buildings and heat emissions from their elements rely on manual inspection and as such are slow, and labor intensive. This is often a daunting task, which requires several days of on-site inspection. In this paper, we propose a fully automatic approach to construct a 3D thermal point cloud of the building interior reflecting the geometry including walls, floors, and ceilings, as well as structures such as furniture, lights, windows, and plug loads. Our approach is based on a wearable ambulatory backpack comprising multiple sensors such as Light Detection And Ranging (LiDAR) scanners, and Infrared and optical cameras. As the operator wearing the backpack walks through the building, the LiDAR scans are collected and processed in order to compute the 3D geometry of the building. Furthermore, the Infrared cameras are calibrated intrinsically and extrinsically such that the captured images are registered to the captured geometry. Thus, the temperature data in the Infrared images is associated with the geometry resulting in a “thermal 3D point cloud”. The same process can be repeated using optical imagery resulting in a “visible 3D point cloud”. By visualizing the two point clouds simultaneously in interactive rendering tools, we can virtually walk through the thermal and optical 3D point clouds, toggle between them, identify and annotate, “hot” regions, objects, plug loads, thermal and moisture leaks, and document their location with fine spatial granularity in the 3D point clouds.

The Iterative Closest Point (ICP) algorithm is widely used to register two roughly aligned surfaces. Its most expensive step is the search for the closest point. Many efficient variants have been proposed to speed up this step, however they do not guarantee that the closest point on a triangulated surface is found. Instead they might result in an approximation of the closest point. In this paper we present a method for the closest point search that is fast and exact. The method was implemented and used to evaluate the accuracy of head motion estimation using dense 3D data obtained from a stereo camera system. The results show that the accuracy of the estimated head motion is better than 1 mm for translational movements and better than 1 degree for rotations. Key-Words: Iterative Closest Point, 3D Registration, Stereo, Head Motion Estimation

In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.

The development of new Simultaneous Localization and Mapping (SLAM) techniques is quickly advancing in research communities and rapidly transitioning into commercial products. Creating accurate and high-quality SLAM maps relies on a robust initialization process. However, the robustness and usability of SLAM initialization for end-users has often been disregarded. This paper presents and evaluates a novel tracking system for 6DOF pose tracking between a single keyframe and the current camera frame, without any prior scene knowledge. Our system is particularly suitable for SLAM initialization, since it allows 6DOF pose tracking in the intermediate frames before a wide-enough baseline between two keyframes has formed. We investigate how our tracking system can be used to interactively guide users in performing an optimal motion for SLAM initialization. However, our findings from a pilot study indicate that the need for such motion can be completely hidden from the user and outsourced to our tracking system. Results from a second user study show that letting our tracking system create a SLAM map as soon as possible is a viable and usable solution. Our work provides important insight for SLAM systems, showing how our novel tracking system can be integrated with a user interface to support fast, robust and user-friendly SLAM initialization.

We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.

We propose to combine recent Convolutional Neural Networks (CNN) models with depth imaging to obtain a reliable and fast multi-person pose estimation algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis is that depth images contain less structures and are easier to process than RGB images while keeping the required information for human detection and pose inference, thus allowing the use of simpler networks for the task. Our contributions are threefold. (i) we propose a fast and efficient network based on residual blocks (called RPM) for body landmark localization from depth images; (ii) we created a public dataset DIH comprising more than 170k synthetic images of human bodies with various shapes and viewpoints as well as real (annotated) data for evaluation; (iii) we show that our model trained on synthetic data from scratch can perform well on real data, obtaining similar results to larger models initialized with pre-trained networks. It thus provides a good trade-off between performance and computation. Experiments on real data demonstrate the validity of our approach.

In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multiclass SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.

This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-tosurface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations. 2009 Elsevier B.V. All rights reserved.


Question: The precision and detail of Kinect depth information for indoor mapping purposes.

This paper explores the potential of RGB-D cameras in the realm of robotics, specifically for the creation of dense 3D models of indoor environments, which are crucial for applications such as robot navigation, manipulation, semantic mapping, and telepresence. RGB-D cameras, which capture RGB images and per-pixel depth data simultaneously, are poised to revolutionize the field with their upcoming availability in high-quality, low-cost models designed for gaming and home entertainment. Unlike traditional mapping methods that use laser range scans, stereo cameras, monocular cameras, or unsorted photo collections, RGB-D cameras offer a new level of detail in 3D mapping. However, they also present challenges: limited depth range (often under 5m), higher depth noise compared to laser scanners, and a narrower field of view (∼60°) relative to specialized cameras or laser scanners (∼180°). Using the PrimeSense camera as a case study, our investigation reveals several key insights. Firstly, standard frame matching techniques are inadequate for robust visual odometry with RGB-D cameras. Secondly, a comprehensive integration of depth and color data significantly improves frame matching and loop closure detection. Thirdly, by leveraging advanced techniques from Simultaneous Localization and Mapping (SLAM) and computer graphics, it's possible to construct and visualize highly accurate and detailed 3D maps with these cameras. Lastly, the feasibility of developing comprehensive robot navigation and interaction systems solely based on affordable depth cameras is demonstrated.

The Kinect sensor is an attachment for the Xbox gaming console which allows players to interact with games through body movement. This paper explores the possibility of using the Kinect in a clinical setting for detection of Parkinson's, postural, flapping, titubation and voice tremors. All of the physical tremors were simulated and the ability of the Kinect to capture them was evaluated. The Kinect was also used to record voice data from real voice tremors patients. Physical and voice data were gathered from healthy persons for comparison. The results showed that the Kinect could reliably detect voice, postural and Parkinson's tremors. A very consistent graph could be obtained repeatedly for both Parkinson's and postural tremors. For voice tremors there was also a consistent pattern that differentiated a normal voice from one with a tremor. We have therefore proven that the Kinect can consistently record Parkinson's, postural and voice tremors.

3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.

Sign Language is a widely used method of communication among the community of deaf-mute people. It contains some series of body gestures, which enables a person to interact without the need of spoken words. Although the use of sign language is very popular among the deaf-mute people but the other communities don't even try to learn it, this creates a gulf of communication and hence becomes a cause of the isolation of physically impaired people. The problem creates a requirement of a system which can facilitate a way of communication between these two communities. This paper presents a novel method for identification of an isolated sign language gesture using Microsoft Kinect. This paper presents the way of extracting some highly robust features from the depth image provided by Kinect and to use them in creating a robust and accurate gesture recognition system, for the purpose of sign language translation. Apart from translation, the proposed system also opens the doors of endless applications in the field of Human Computer Interaction. The proposed algorithm helps in translating a sign language gesture performed by a user, which in-turn can be used as an input for different types of applications.

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.

Visual Simultaneous Localization and Mapping (VSLAM) requires feature detection on visual data. In indoor scenes that include architectures such as plain walls and doors, there are no or less corners detected, in such cases tracking will be lost. To overcome this situation we track different types of features that help in feature extraction even in textureless scenes. Line features are used to get the outline of the structure. This paper explains method of line based localization when no point features are detected and process of mapping the matched lines between two frames. One of the existing state of art monocular SLAM that extracts point features only, is used as the main build and LBD (Line Band Descriptor) is incorporated in it for localization and mapping. Our new system extracts more features compared to the existing system and complexity of the bundle adjustment is decreased as it decreases the keyframes selected for local mapping and optimization when tested using our own dataset and compared with the existing system.

In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a manifold map, comprised of overlapping Truncated Signed Distance Field (TSDF) volumes. These volumes are localized through featurebased bundle adjustment. Our main contribution is to use a probabilistic measure to identify stable regions in the map, and fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on publicly available datasets, as well as on a number of our own datasets, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps.

Robot localization is one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we can successfully identify the room in which the robot is located in 80% of the tests.

With the rapid development and wide spread of the virtual reality technology, we can easily find VR systems in various places such as school, library and home. Translation and rotation, the most frequently used gestures for manipulating objects in the real world, need to be implemented in order to make a user feel comfortable while manipulating 3D objects in the systems. In this paper, we propose a set of user-friendly 3D object manipulation gestures and develop a recognizer using Kinect for the VR systems. The usefulness and suitableness of the proposed system is shown from the user study performed.

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.