Question: Cyclostationary feature recognition in cognitive radio employing various modulation techniques.

This paper explores the concept of cognitive radio, presenting it as a transformative solution to enhance the utilization of the radio electromagnetic spectrum, a critical natural resource. Envisioned as an intelligent wireless communication system, cognitive radio, underpinned by software-defined radio technology, is characterized by its environmental awareness and adaptive capabilities. This system employs the principle of understanding-by-building to glean insights from its surroundings and dynamically adjust to the statistical fluctuations in input stimuli, driven by dual primary objectives: /spl middot/ Ensuring highly dependable communication access whenever and wherever necessary; /spl middot/ Optimizing the utilization of the radio spectrum. The discussion delves into the significance of interference temperature as a novel metric for quantifying and managing interference. The paper then delineates three pivotal cognitive functions: 1) Analysis of the radio scene, which involves comprehending the current spectrum environment; 2) Estimation of channel state and predictive modeling, aimed at forecasting channel conditions; and 3) Implementation of transmit-power control and dynamic spectrum management, focusing on efficient resource allocation. The paper also contemplates the emergent behavior of cognitive radio systems, highlighting their potential for transformative impact on wireless communications.

This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auction-based algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.

High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.

In this paper, a new minimum mean square error (MMSE) based detector is presented for multi-message detection in both conventional spread spectrum (SS) watermarking and improved spread spectrum (ISS) watermarking. The proposed detector yields an improved detection performance in terms of bit error rate (BER) for both SS and ISS multiple message embedding. Simulation results demonstrate that the proposed MMSE detector outperforms the conventional detector, the matched filter detector, by orders of magnitude.

A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFTclustering identifies the coincidence of spectral features overmost channels of amulti-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented. © 2014 Elsevier B.V. All rights reserved.

In this paper LDS-OFDM is introduced as an uplink multicarrier multiple access scheme. LDS-OFDM uses Low Density Signature (LDS) structure for spreading the symbols in frequency domain. This technique benefits from frequency diversity besides its ability of supporting parallel data streams up to 400% more than the number of subcarriers (overloaded condition). The performance of LDS-OFDM is evaluated and compared with conventional OFDMA systems over multipath fading channel. Monte Carlo based simulations for various loading conditions indicate significant performance improvement over OFDMA system.

Recently, Wi-Fi has caught tremendous attention for its ubiquity. Motivated by Wi-Fi’s low cost and privacy preservation, researchers have been putting lots of investigation into its potential on action recognition and even person identification. In this paper, for bringing a new modality for multimedia community, we offer an comprehensive overview on these two topics in Wi-Fi. Also, through looking at these two topics from an unprecedented perspective, we could achieve generality instead of designing specific ad-hoc features for each scenario. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on computer vision methods. To minimize the effect of location dependency embedded in CSI, we propose a novel de-noising method based on Singular Value Decomposition (SVD) to eliminate the background energy and effectively extract the channel information of signals reflected by human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed methods. Also, we conclude the factors that would affect the performance and highlight a few potential issues that require further deliberations.

The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.

We describe a new carrier–sense multiple access (CSMA) protocol for multihop wireless networks, sometimes also called ad hoc networks. The CSMA protocol divides the available bandwidth into several channels and selects an id le channel randomly for packet transmission. It also employs a notion of “soft” channel reservation as it gives preference to the channel that was used for the last successful transmission. We show via simulations that this multichannel CSMA protocol provides a higher throughput compared to its single channel counterpart by reducing the packet loss due to collisions. We also show that the use of channel reservation provides better performance than multichannel CSMA with purely random idle channel selection.

This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an emotion pool that an instance of the test subject can be assigned to; and (3) in the final stage, generating the predicted emotion from the given emotion pool for the test instance. In comparison with a series of alternative methods, the high accuracy of 70.04% achieved by our proposed method clearly demonstrates the potential of the three-stage decision method in multi-subject emotion recognition.

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.


Question: Cyclostationary feature recognition in cognitive radio employing various modulation techniques.

This document explores the concept of cooperative sensing among cognitive radios to address the challenge of opportunistic spectrum usage in under-utilized frequency bands. Cognitive radios are equipped with the capability to sense the spectrum and utilize available frequency bands when the primary user is not present. However, achieving the necessary sensitivity levels can be challenging due to potential deep fades that individual radios might encounter. To mitigate this issue, a cooperative sensing approach based on hard decisions is proposed, which significantly reduces the sensitivity requirements on each radio by leveraging the collective sensing efforts of multiple radios.

The paper demonstrates that the "link budget" typically reserved for handling fading conditions is closely tied to the probability of detection required by the system. With a modest number of cooperating radios (approximately 10-20), facing independent fades, practical threshold levels can be achieved by substantially lowering the individual detection requirements. Surprisingly, hard decisions perform nearly as effectively as soft decisions in achieving these cooperative gains.

In scenarios where shadowing is correlated among the cooperating radios, the cooperative gains are limited by the cooperation footprint, which is the area where users collaborate. A key finding is that a small group of independent users is more resilient than a larger number of users experiencing correlated fades. However, cooperative gains are highly sensitive to the presence of adversarial or failing cognitive radios. Radios that fail in a predictable manner (consistently reporting the presence or absence of a primary user) can be managed through censorship. Conversely, radios that fail in unpredictable ways or exhibit malicious behavior introduce a limit on the achievable sensitivity reductions. As a guideline, if there is a belief that 1/N of the users might fail in an unanticipated manner, the cooperation gains are capped at what could be attained with N trusted users. This highlights the importance of trust and reliability in cooperative sensing environments.

In this paper initial downlink synchronization (sync) and cell identification algorithms for the 3rd generation partnership project (3GPP) long term evolution (LTE) systems are presented. The frequency division duplex (FDD) mode is used in the downlink radio frame structure. A user equipment digital receiver architecture is proposed. The orthogonality of signals in orthogonal frequency division multiplexing (OFDM) may be lost due to some impairments such as frequency, time, and phase offsets. Therefore, major parts of this research are involved in restoring the orthogonality at the user equipment (UE) by using some techniques to estimate the sync parameters, and to detect cell identity among candidate cells based on sync signals. A Farrow structure interpolator is used to compensate the fractional timing offset. Both inter site synchronous and inter site asynchronous networks are presented. Computer simulations are used to demonstrate the performance of the proposed schemes with multipath Rayleigh fading channel, frequency offsets, timing offsets, and additive white Gaussian noise (AWGN). Results show high probability of cell identification in a very short time 20 ms in both multi cell model scenarios, especially when multiple input multiple output (MIMO) technique, oversampling at the UE, and high order Farrow structure interpolator are used. Key-Words: 3GPP LTE, OFDM, MIMO, Frequency and Time Synchronization, Cell Search

The Multilook Cross Correlation (MLCC) is one of the most reliable algorithms used for Doppler ambiguity number estimation of the Doppler centroid parameter. However, the existing MLCC algorithm is only suitable for low contrast scenes. In high contrast scenes, the estimated result is not reliable, and the error is unacceptable. Besides, the Doppler centroid estimation processing time is long and can only be used in offline processing. In this paper, we introduce a modified MLCC algorithm that has better sensitivity which is suitable not only for low contrast scenes, but also for high contrast scenes. In addition, the modified MLCC algorithm can be implemented on parallel signal processing units for better time efficiency. Experiments with RADARSAT-1 data show that the modified algorithm works well in both high and low contrast scenes.

It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy.

One of 5G communication typical scenarios is machine-to-machine communication with large number of devices, which proposes new requirements for uplink transmission. Filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) is one of the popular nonorthogonal waveform candidates for 5G transmission. In this paper, FBMC/OQAM and cyclic prefix OFDM with single antenna settings are comprehensively evaluated and compared in uplink transmission form four aspects: power spectral density, timing and frequency misalignments, guard bands between adjacent users and time efficiency. At last, our comparison results show that FBMC is more suitable for 5G uplink multi-user asynchronous transmissions than CP-OFDM.

We designed, implemented, tested and measured an ultra low power Wake Up Receiver (WUR), intended for use in Wireless Body Area Networks (WBAN). Gaussian On-Off Keying (GOOK) and Pulse Width Modulation (PWM) are used to modulate and encode, respectively, the preamble signal. The receiver incorporates a decoder to enable Serial Peripheral Interface (SPI). WUR was also comprehensively tested for power consumption and robustness to RF interference from wireless devices commonly found in the vicinity of persons utilising WBAN technology. Our results and comparative evaluation demonstrate that the achieved listening power of 270nW for the Wake Up Receiver is significantly lower power consumption than for the other state-of-the-art. The proposed preamble detection scheme can significantly reduce false wake ups due to other wireless devices in a WBAN. Additionally, SPI significantly reduces the overall power consumption for packet reception and decoding.

We study the 24-hour spectrum usage pattern in Singapore in the frequency bands ranging from 80 MHz to 5850 MHz. The objectives are to find how the scarce radio spectrum allocated to different services is utilized in Singapore and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. The results from the spectrum measurements taken over 12 weekday periods reveal that a significant amount of spectrum in Singapore has very low occupancy all the time. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that Singapore has a great potential for employing emerging spectrum sharing technology such as the cognitive radio technology to accommodate enormous demands for future wireless services. However, this study of spectrum survey is preliminary in its nature and future long term studies need to be performed to determine any potential secondary usage on those channels that have low or no active utilization.

Wi-Fi based fingerprinting systems, mostly utilize the Received Signal Strength Indicator (RSSI), which is known to be unreliable due to environmental and hardware effects. In this paper, we present a novel Wi-Fi fingerprinting system, exploiting the fine-grained information known as Channel State Information (CSI). The frequency diversity of CSI can be effectively utilized to represent a location in both frequency and spatial domain resulting in more accurate indoor localization. We propose a novel location signature CSI-MIMO that incorporates Multiple Input Multiple Output (MIMO) information and use both the magnitude and the phase of CSI of each sub-carrier. We experimentally evaluate the performance of CSI-MIMO fingerprinting using the k-nearest neighbor and the Bayes algorithm. The accuracy of the proposed CSI-MIMO is compared with Finegrained Indoor Fingerprinting System (FIFS) and a simple CSI-based system. The experimental result shows an accuracy improvement of 57% over FIFS with an accuracy of 0.95 meters.

In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the HHH matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix H increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes HTHx, where x is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the HTH matrix. We also propose a simple estimation scheme which directly obtains an estimate of HTH (instead of an estimate of H), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of H. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.

We have developed a gesture recognition system, based on Hidden Markov Models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 di erent gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example.

Inspired by cognitive radio networks, we consider a setting where multiple users share several channels modeled as a multi-user multi-armed bandit (MAB) problem. The characteristics of each channel are unknown and are different for each user. Each user can choose between the channels, but her success depends on the particular channel chosen as well as on the selections of other users: if two users select the same channel their messages collide and none of them manages to send any data. Our setting is fully distributed, so there is no central control. As in many communication systems, the users cannot set up a direct communication protocol, so information exchange must be limited to a minimum. We develop an algorithm for learning a stable configuration for the multi-user MAB problem. We further offer both convergence guarantees and experiments inspired by real communication networks, including comparison to state-of-the-art algorithms.


Question: Cloning of spacetime expressions for the purpose of blendshape creation.

This paper presents an innovative method for the creation of textured 3D facial models, which can be generated automatically from photographs or crafted through a user-friendly interface. The technique addresses two pivotal challenges in computer-assisted facial modeling: the automatic registration of new images or 3D models by establishing a dense, one-to-one correspondence with an internal model, and the regulation of the model's realism to prevent the creation of faces with an unnatural appearance. A morphable face model is developed by converting the shape and texture of a set of 3D face models into a vector space. By combining these prototypes linearly, new faces and expressions can be created. To guide manual modeling and automated matching algorithms, shape and texture constraints are derived from statistical analysis of the example faces. The paper showcases 3D reconstructions from single images and their applications in photo-realistic image modifications. Additionally, it demonstrates the manipulation of faces based on complex attributes such as gender, facial fullness, or distinctiveness.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: An efficient heuristic for economic dispatch with valve-point effects
This paper presents a heuristic algorithm for solving the economic dispatch problem (EDP) with valve-point effects. The proposed algorithm is based on the concept of a modified particle swarm optimization (PSO) technique. The main objective is to find the optimal power generation for each generating unit in order to minimize the total fuel cost while satisfying the constraints of the system. The proposed algorithm is tested on a system with 10 generating units and compared with other methods in the literature. The results show that the proposed algorithm is more efficient and provides better solutions than other methods.

**Similar Documents**: This paper introduces an effective heuristic algorithm for tackling the economic dispatch problem (EDP), specifically addressing valve-point effects. The algorithm is an innovative adaptation of the particle swarm optimization (PSO) technique, tailored to optimize power generation from each unit to minimize total fuel costs, all while adhering to system constraints. The algorithm's performance is evaluated on a 10-unit system, and the outcomes are contrasted with those from existing methods in the field. The findings reveal that the proposed algorithm outperforms other techniques, offering superior efficiency and solution quality.

KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.

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.

Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatiotemporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks (RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.

Zoomorphic shapes are man-made shapes that possess the form or appearance of an animal. They have desirable aesthetic properties, but are difficult to create using conventional modeling tools. We present a method for creating zoomorphic shapes by merging a man-made shape and an animal shape. To identify a pair of shapes that are suitable for merging, we use an efficient graph kernel based technique. We formulate the merging process as a continuous optimization problem where the two shapes are deformed jointly to minimize an energy function combining several design factors. The modeler can adjust the weighting between these factors to attain high-level control over the final shape produced. A novel technique ensures that the zoomorphic shape does not violate the design restrictions of the man-made shape. We demonstrate the versatility and effectiveness of our approach by generating a wide variety of zoomorphic shapes.

Procedural content generation is becoming an increasingly popular research area as a means of algorithmically generating scene content for virtual environments. The automated generation of such content avoids the manual labour typically associated with creating scene content, and is extremely useful in application areas such as computer graphics, movie production and video games. While virtual 3D caves are commonly featured in these virtual environment applications, procedural cave generation is not an area that has received much attention among researchers to date. This paper presents a procedural approach to generating 3D cave structures. Other than the development of a method to effectively automate the generation of visually believable 3D cave models, this paper also investigates how to efficiently construct and store this spatial information using a voxel-based octree data structure. In addition, the proposed approach demonstrates that caves with different characteristics can be generated by adjusting certain parameters in order to facilitate the creation of diverse cave structures.

This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

In this paper we introduce a template-based method for recognizing human actions called action MACH. Our approach is based on a maximum average correlation height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.

We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.

In computer animation, the proportional-derivative (PD) controller is a common technique for tracking characters' motion. A new formulation of the PD controller-stable PD (SPD)-allows arbitrarily high gains, even at large time steps. The key is to determine joint forces and torques while taking into account the character's positions and velocities in the next time step. SPD is stable even when combined with a physics simulator that uses simple Euler integration. SPD controllers have a variety of uses, including motion tracking in a physics simulator, keyframe interpolation with secondary motion, and constraint satisfaction for simulation.


Question: Cloning of spacetime expressions for the purpose of blendshape creation.

This paper introduces a user-friendly and efficient system for the interactive creation and manipulation of 3D facial expressions. The system allows users to design and modify facial expressions through various intuitive methods: drawing freehand strokes, defining distances between specific facial landmarks, incrementally editing curves on the face, or directly manipulating facial points in a 2D screen environment. The challenge in developing this interface lies in preventing the creation of unnatural expressions, which can easily arise from user input. To address this, we employ a maximum a posteriori framework, combining the user's input with a set of facial expression priors derived from a comprehensive database. This approach ensures that the generated facial expression is both optimal and natural, closely aligning with the user's intended outcome. The effectiveness of our system is assessed through a detailed comparison with alternative facial modeling techniques. Additionally, we conduct a user study, comparing our system with leading facial expression modeling software (Poser 7), to showcase its usability and superior performance.

We describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the potprofile to the shape of the user’s hand represented as a point-cloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Through our approach, we address two specific problems: (a) determining start and end of deformation without explicit clutching and declutching, and (b) identifying user’s intent by characterizing grasp and motion of the hand on the pot. We evaluated our approach’s performance in terms of intent classification, users’ behavior, and users’ perception of controllability. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping and deforming the pot. Further, the use of PCL as mid-air input allows for using common physical objects as tools for pot deformation. Users particularly enjoyed this aspect of our method for shaping pots.

We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor’s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.

The accurate prediction of the post-surgical facial shape is of paramount importance for surgical planning in facial surgery. In this paper we present a framework for facial surgery simulation which is based on volumetric finite element modeling. We contrast conventional procedures for surgical planning against our system by accompanying a patient during the entire process of planning, medical treatment and simulation. In various preprocessing steps a 3D physically based facial model is reconstructed from CT and laser range scans. All geometric and topological changes are modeled interactively using Alias.#8482; Applying fully 3D volumetric elasticity allows us to represent important volumetric effects such as incompressibility in a natural and physically accurate way. For computational efficiency, we devised a novel set of prismatic shape functions featuring a globally C1-continuous surface in combination with a C0 interior. Not only is it numerically accurate, but this construction enables us to compute smooth and visually appealing facial shapes.

This paper describes the methods and data structures used for generating new human motions out of existing motion data. These existing animation data are derived from live motion capture or produced through the traditional animation tools. We present a survey of techniques for generating new animations by physical and emotional parameterizations. Discussed new animations are generated by Fourier transformations, transformations of velocity and amplitude and scattered data interpolation in this report.

We propose a method to transfer both head poseand facial expression of a source person in a video to the faceof a target person in an output video. Our method models theentire 2D frame instead of the 3D face, and it generates outputresults using a status score, which includes the relative facialstatus about the head pose and expression in a frame. From thetarget video, the learning process obtains frame features neededfor moving to each frame from the neutral frame for all frames,and generates the basis of these features via principal componentanalysis (PCA). Then, it learns to generate these features from agiven status score sequentially. In the transfer process, it obtainsa status score from a source frame of the video and generatesthe features from the given status score. Then, it generates theoutput frame using the reconstructed features. An output videois generated by repeating these steps for each source frame.Our method generates output results on the trajectory of thetarget video by using the advantage of PCA. Therefore, in theoutput results generated by our methods, both head pose andexpression are transferred correctly while the non-face regionsof the frames are supported. Finally, we experimentally comparethe effectiveness of our method and conventional methods.

In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.

In recent years, projection mapping has attracted much attention in a variety of fields. Generally, however, the objects in projection mapping are limited to rigid and static or quasistatic objects. Dynamic projection mapping onto a deformable object could remarkably expand the possibilities. In order to achieve such a projection mapping, it is necessary to recognize the deformation of the object even when it is occluded. However, it is still a challenging problem to achieve this task in real-time with low latency. In this paper, we propose an efficient, high-speed tracking method utilizing high-frame-rate imaging. Our method is able to track an array of dot markers arranged on a deformable object even when there is external occlusion caused by the user interaction and self-occlusion caused by the deformation of the object itself. Additionally, our method can be applied to a stretchable object. Dynamic projection mapping with our method showed robust and consistent display onto a sheet of paper and cloth with a tracking performance of about 0.2 ms per frame, with the result that the projected pattern appeared to be printed on the deformable object.

Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator’s output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, OMNIGLOT, CELEBA) and synthetic 3D datasets. A video of the agent can be found at https://youtu.be/iSyvwAwa7vk.

This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.


Question: Cloning of spacetime expressions for the creation of blendshapes.

This paper introduces a novel approach for high-quality passive and markerless facial performance capture, utilizing anchor frames for improved accuracy and efficiency. Our technique begins with the acquisition of per-frame geometry at high resolution using cutting-edge stereo reconstruction methods. Following this, a single triangle mesh is established and propagated throughout the entire performance sequence. Recognizing the repetitive nature of facial expressions within performances, we identify anchor frames—those that closely resemble a manually selected reference expression. These anchor frames are automatically calculated across one or multiple performances, enabling a robust image-space tracking method that computes pixel matches from the reference frame to all anchor frames, and subsequently to the rest of the sequence via sequential matching. This method allows for the parallel propagation of a single reconstructed frame to an entire sequence, in contrast to previous sequential approaches. Our anchored reconstruction strategy not only mitigates tracker drift but also effectively manages occlusions and motion blur, ensuring high-quality results. Moreover, the parallel tracking and mesh propagation significantly reduce computation times. Our technique is versatile enough to automatically match anchor frames across different sequences captured at various times, seamlessly propagating a single mesh to all performances, thereby enhancing the overall facial performance capture process.

We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.

We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.

3D morphable models are low-dimensional parameterizations of 3D object classes which provide a powerful means of associating 3D geometry to 2D images. However, morphable models are currently generated from 3D scans, so for general object classes such as animals they are economically and practically infeasible. We show that, given a small amount of user interaction (little more than that required to build a conventional morphable model), there is enough information in a collection of 2D pictures of certain object classes to generate a full 3D morphable model, even in the absence of surface texture. The key restriction is that the object class should not be strongly articulated, and that a very rough rigid model should be provided as an initial estimate of the “mean shape.” The model representation is a linear combination of subdivision surfaces, which we fit to image silhouettes and any identifiable key points using a novel combined continuous-discrete optimization strategy. Results are demonstrated on several natural object classes, and show that models of rather high quality can be obtained from this limited information.

This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.

We present a method to acquire the reflectance field of a human face and use these measurements to render the face under arbitrary changes in lighting and viewpoint. We first acquire images of the face from a small set of viewpoints under a dense sampling of incident illumination directions using a light stage. We then construct a reflectance function image for each observed image pixel from its values over the space of illumination directions. From the reflectance functions, we can directly generate images of the face from the original viewpoints in any form of sampled or computed illumination. To change the viewpoint, we use a model of skin reflectance to estimate the appearance of the reflectance functions for novel viewpoints. We demonstrate the technique with synthetic renderings of a person's face under novel illumination and viewpoints.

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.

3D Euler spirals are visually pleasing, due to their property of having their curvature and their torsion change linearly with arc-length. This paper presents a novel algorithm for fitting piecewise 3D Euler spirals to 3D curves with G2 continuity and torsion continuity. The algorithm can also handle sharp corners. Our piecewise representation is invariant to similarity transformations and it is close to the input curves up to an error tolerance.

Recent technological advances in connected-speech recognition and position sensing in space have encouraged the notion that voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality.
 The work described herein involves the user commanding simple shapes about a large-screen graphics display surface. Because voice can be augmented with simultaneous pointing, the free usage of pronouns becomes possible, with a corresponding gain in naturalness and economy of expression. Conversely, gesture aided by voice gains precision in its power to reference.

We present an interactive material acquisition system for average users to capture the spatially-varying appearance of daily objects. While an object is being scanned, our system computes its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially-varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture

This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.


Question: Cloning of spacetime expressions for the creation of blendshapes.

In the realm of feature-based vision systems, the identification and consistent tracking of features across frames is paramount. While tracking algorithms have reached a high level of sophistication, the challenge remains in selecting features that are not only robust to tracking but also accurately correspond to physical points within the real world. This paper introduces a novel feature selection criterion that is inherently optimal, designed in accordance with the operational principles of the tracking algorithm. Additionally, we present a feature monitoring technique capable of identifying occlusions, disocclusions, and features that fail to represent actual points in the environment. These innovations are underpinned by an advanced tracking algorithm that builds upon Newton-Raphson style search methods, extending their capabilities to handle affine image transformations. The efficacy of our methods is substantiated through a series of simulations and experimental evaluations, showcasing their superiority in feature tracking and monitoring.

We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.

There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs). The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques. To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time. Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions. These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement. To map the input signals to a 3D face model, we perform a single-instance offline training session for each person. For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use. The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds.

High-level manipulation of facial expressions in images — such as changing a smile to a neutral expression — is challenging because facial expression changes are highly non-linear, and vary depending on the appearance of the face. We present a fully automatic approach to editing faces that combines the advantages of flow-based face manipulation with the more recent generative capabilities of Variational Autoencoders (VAEs). During training, our model learns to encode the flow from one expression to another over a low-dimensional latent space. At test time, expression editing can be done simply using latent vector arithmetic. We evaluate our methods on two applications: 1) single-image facial expression editing, and 2) facial expression interpolation between two images. We demonstrate that our method generates images of higher perceptual quality than previous VAE and flow-based methods.

Computer Graphics and Geometric Ornamental Design

In this paper we consider the problem of computing the 3D shape of an unknown, arbitrarily-shaped scene from multiple photographs taken at known but arbitrarily-distributed viewpoints. By studying the equivalence class of all 3D shapes that reproduce the input photographs, we prove the existence of a special member of this class, the photo hull, that (1) can be computed directly from photographs of the scene, and (2) subsumes all other members of this class. We then give a provably-correct algorithm, called Space Carving, for computing this shape and present experimental results on complex real-world scenes. The approach is designed to (1) capture photorealistic shapes that accurately model scene appearance from a wide range of viewpoints, and (2) account for the complex interactions between occlusion, parallax, shading, and their view-dependent effects on scene-appearance.

We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.

We present a new motion tracking technique to robustly reconstruct non-rigid geometries and motions from a single view depth input recorded by a consumer depth sensor. The idea is based on the observation that most non-rigid motions (especially human-related motions) are intrinsically involved in articulate motion subspace. To take this advantage, we propose a novel <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphic xlink:href="xu-ieq2-2688331.gif"/></alternatives></inline-formula> based motion regularizer with an iterative solver that implicitly constrains local deformations with articulate structures, leading to reduced solution space and physical plausible deformations. The <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math> <alternatives><inline-graphic xlink:href="xu-ieq3-2688331.gif"/></alternatives></inline-formula> strategy is integrated into the available non-rigid motion tracking pipeline, and gradually extracts articulate joints information online with the tracking, which corrects the tracking errors in the results. The information of the articulate joints is used in the following tracking procedure to further improve the tracking accuracy and prevent tracking failures. Extensive experiments over complex human body motions with occlusions, facial and hand motions demonstrate that our approach substantially improves the robustness and accuracy in motion tracking.

Facial scanning has become ubiquitous in digital media, but so far most efforts have focused on reconstructing the skin. Eye reconstruction, on the other hand, has received only little attention, and the current state-of-the-art method is cumbersome for the actor, time-consuming, and requires carefully setup and calibrated hardware. These constraints currently make eye capture impractical for general use. We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet. This is accomplished with a new parametric model of the eye built from the database, and a novel image-based model fitting algorithm. Our method provides both automatic reconstructions of real eyes, as well as artistic control over the parameters to generate user-specific eyes.

Stroke correspondence construction is a precondition for vectorized 2D animation inbetweening and remains a challenging problem. This paper introduces the FTP-SC, a fuzzy topology preserving stroke correspondence technique, which is accurate and provides the user more effective control on the correspondence result than previous matching approaches. The method employs a two-stage scheme to progressively establish the stroke correspondence construction between the keyframes. In the first stage, the stroke correspondences with high confidence are constructed by enforcing the preservation of the so-called “fuzzy topology” which encodes intrinsic connectivity among the neighboring strokes. Starting with the high-confidence correspondences, the second stage performs a greedy matching algorithm to generate a full correspondence between the strokes. Experimental results show that the FTP-SC outperforms the existing approaches and can establish the stroke correspondence with a reasonable amount of user interaction even for keyframes with large geometric and spatial variations between strokes. CCS Concepts •Computing methodologies → Animation; Parametric curve and surface models;

This paper addresses the problem of parametric representation and estimation of complex planar curves in 2-D, surfaces in 3-D and nonplanar space curves in 3-D. Curves and surfaces can be defined either parametrically or implicitly, and we use the latter representation. A planar curve is the set of zeros of a smooth function of two variables X-Y, a surface is the set of zeros of a smooth function of three variables X-~-Z, and a space curve is the intersection of two surfaces, which are the set of zeros of two linearly independent smooth functions of three variables X-!/-Z. For example, the surface of a complex object in 3D can be represented as a subset of a single implicit surface, with similar results for planar and space curves. We show how this unified representation can be used for object recognition, object position estimation, and segmentation of objects into meaningful subobjects, that is, the detection of “interest regions” that are more complex than high curvature regions and, hence, more useful as features for object recognition. Fitting implicit curves and surfaces to data would be ideally based on minimizing the mean square distance from the data points to the curve or surface. Since the distance from a point to a curve or surface cannot be computed exactly by direct methods, the approximate distance, which is a first-order approximation of the real distance, is introduced, generalizing and unifying previous results. We fit implicit curves and surfaces to data minimizing the approximate mean square distance, which is a nonlinear least squares problem. We show that in certain cases, this problem reduces to the generalized eigenvector fit, which is the minimization of the sum of squares of the values of the functions that define the curves or surfaces under a quadratic constraint function of the data. This fit is computationally reasonable to compute, is readily parallelizable, and, hence, is easily computed in real time. In general, the generalized eigenvector lb provides a very good initial estimate for the iterative minimization of the approximate mean square distance. Although we are primarily interested in the 2-D and 3-D cases, the methods developed herein are dimension independent. We show that in the case of algebraic curves and surfaces, i.e., those defined by sets of zeros of polynomials, the minimizers of the approximate mean square distance and the generalized eigenvector fit are invariant with respect to similarity transformations. Thus, the generalized eigenvector lit is independent of the choice of coordinate system, which is a very desirable property for object recognition, position estimation, and the stereo matching problem. Finally, as applications of the previous techniques, we illustrate the concept of “interest regions” Manuscript received April 13, 1988; revised April 30, 1991. This work was supported by an IBM Fellowship and NSF Grant IRI-8715774. The author was with the Laboratory for Engineering Man/Machine Systems, Department of Engineering, Brown University, Providence, RI 02912. He is now with the Exploratory Computer Vision Group, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598. IEEE Log Number 9102093. for object recognition and describe a variable-order segmentation algorithm that applies to the three cases of interest.


Question: Cloning of spacetime expressions for the purpose of blendshape creation.

This paper presents a hierarchical framework for estimating motion information, designed to compute various representations of motion data. Central to this framework is a dual-model approach: a global model that dictates the overarching structure of the estimated motion, and a local model employed during the estimation process. Additionally, the framework incorporates a coarse-to-fine refinement strategy to enhance accuracy. The paper delves into four distinct motion models: affine flow, planar surface flow, rigid body motion, and general optical flow. Each model is applied to specific case studies, showcasing their effectiveness in different scenarios. This hierarchical model-based motion estimation technique offers a versatile solution for motion analysis, adaptable to a wide range of applications.

We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.

ÐA new view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal templateÐa static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms. Index TermsÐMotion recognition, computer vision.

An explicit three-dimensional representation is constructed from feature-points extracted from a sequence of images taken by a moving camera. The points are tracked through the sequence, and their 3D locations accurately determined by use of Kalman Filters. The ego-motion of the camera is also solved for.

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.

This paper introduces a novel surface-modeling method to stochastically distribute features on arbitrary topological surfaces. The generated distribution of features follows the Poisson disk distribution, so we can have a minimum separation guarantee between features and avoid feature overlap. With the proposed method, we not only can interactively adjust and edit features with the help of the proposed Poisson disk map, but can also efficiently re-distribute features on object surfaces. The underlying mechanism is our dual tiling scheme, known as the dual Poisson-disk tiling. First, we compute the dual of a given surface parameterization, and tile the dual surface by our specially-designed dual tiles; during the pre-processing, the Poisson disk distribution has been pre-generated on these tiles. By dual tiling, we can nicely avoid the problem of corner heterogeneity when tiling arbitrary parameterized surfaces, and can also reduce the tile set complexity. Furthermore, the dual tiling scheme is non-periodic, and we can also maintain a manageable tile set. To demonstrate the applicability of this technique, we explore a number of surface-modeling applications: pattern and shape distribution, bump-mapping, illustrative rendering, mold simulation, the modeling of separable features in texture and BTF, and the distribution of geometric textures in shell space.

Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.

This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

We present the design and construction of a handheld mirror simulation device. The perception of the world reflected through a mirror depends on the viewer's position with respect to the mirror and the 3-D geometry of the world. In order to simulate a real mirror on a computer screen, images of the observed world, consistent with the viewer's position, must be synthesized and displayed in realtime. Our system is build around a LCD screen manipulated by the user, a single camera fixed on the screen, and a tracking device. The continuous input video stream and tracker data is used to synthesize, in real-time, a continuous video stream displayed on the LCD screen. The synthesized video stream is a close approximation of what the user would see on the screen surface if it were a real mirror. Our system provides a generic interface for applications involving rich, first-person interaction, such as the Virtual Daguerreotype.

We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.

In this paper, we explore the problem of enhancing still pictures with subtly animated motions. We limit our domain to scenes containing passive elements that respond to natural forces in some fashion. We use a semi-automatic approach, in which a human user segments the scene into a series of layers to be individually animated. Then, a "stochastic motion texture" is automatically synthesized using a spectral method, i.e., the inverse Fourier transform of a filtered noise spectrum. The motion texture is a time-varying 2D displacement map, which is applied to each layer. The resulting warped layers are then recomposited to form the animated frames. The result is a looping video texture created from a single still image, which has the advantages of being more controllable and of generally higher image quality and resolution than a video texture created from a video source. We demonstrate the technique on a variety of photographs and paintings.


Question: Enhanced Fraud Detection: Identifying Credit Card Fraud through Clustering Data Mining Methods

Introducing FraudMiner: An Advanced Credit Card Fraud Detection System Utilizing Frequent Itemset Mining Techniques

In this paper, we present an innovative credit card fraud detection system, FraudMiner, designed to effectively identify fraudulent activities in highly imbalanced and anonymous credit card transaction datasets. The system tackles the class imbalance challenge by employing frequent itemset mining to discern both legal and fraudulent transaction patterns for each cardholder. A unique matching algorithm is then applied to determine whether a new transaction is more closely aligned with legal or fraudulent patterns, enabling accurate classification. To address the dataset's anonymity, all attributes are considered equally significant in the pattern discovery process, without bias. The efficacy of FraudMiner is demonstrated through its performance on the UCSD Data Mining Contest 2009 Dataset, where it exhibits a superior fraud detection rate, balanced classification rate, Matthews correlation coefficient, and significantly lower false alarm rate in comparison to existing advanced classifiers. This showcases FraudMiner as a robust and efficient solution for credit card fraud detection in real-world scenarios.

BACKGROUND
We aimed to identify the indicators of healthcare fraud and abuse in general physicians' drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse.

METHODS
We applied data mining approach to a major health insurance organization dataset of private sector general physicians' prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach.

RESULTS
Thirteen indicators were developed in total. Over half of the general physicians (54%) were 'suspects' of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data.

CONCLUSION
Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.

Outlier Detection is a Data Mining Application. Outlier contains noisy data which is researched in various domains. The various techniques are already being researched that is more generic. We surveyed on various techniques and applications of outlier detection that provides a novel approach that is more useful for the beginners. The proposed approach helps to clean data at university level in less time with great accuracy. This survey includes the existing outlier techniques and applications where the noisy data exists. Our paper defines critical review on various techniques used in different applications of outlier detection that are to be researched further and they gives a particular type of knowledge based data i.e. more useful in research activities. So where the Anomalies is present it will be detected through outlier detection techniques and monitored accordingly especially in educational Data Mining.

Introduction Breast cancer (BC) is the most common cancer in women, affecting about 10% of all women at some stages of their life. In recent years, the incidence rate keeps increasing and data show that the survival rate is 88% after five years from diagnosis and 80% after 10 years from diagnosis [1]. Early prediction of breast cancer is one of the most crucial works in the follow-up process. Data mining methods can help to reduce the number of false positive and false negative decisions [2,3]. Consequently, new methods such as knowledge discovery in databases (KDD) has become a popular research tool for medical researchers who try to identify and exploit patterns and relationships among large number of variables, and predict the outcome of a disease using historical cases stored in datasets [4].

WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS

This paper deals with nding outliers (exceptions) in large datasets. The identiication of outliers can often lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. One contribution of this paper is to show how our proposed, intuitive notion of outliers can unify or generalize many of the existing notions of outliers provided by discordancy tests for standard statistical distributions. Thus, when mining large datasets containing many attributes, a uniied approach can replace many statistical discordancy tests, regardless of any knowledge about the underlying distribution of the attributes. A second contribution of this paper is the development of an algorithm to nd all outliers in a dataset. An important advantage of this algorithm is that its time complexity is linear with respect to the number of objects in the dataset. We include preliminary performance results.

The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.

Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Nowadays, more and more organizations keep their valuable and sensitive data in Database Management Systems (DBMSs). The traditional database security mechanisms such as access control mechanisms, authentication, data encryption technologies do not offer a strong enough protection against the exploitation of vulnerabilities (e.g. intrusions) in DBMSs from insiders. Intrusion detection systems recently proposed in the literature focus on statistical approaches, which are not intuitive. Our research is the first ever effort to use process mining modeling low-level event logs for database intrusion detection. We have proposed a novel approach for visualizing database intrusion detection using process mining techniques. Our experiments showed that intrusion detection visualization will be able to help security officers who might not know deeply the complex system, identify the true positive detection and eliminate the false positive results.

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.