Question: An ensemble method using exemplar-SVMs for object detection and other applications.

This paper presents a novel technique for object recognition and shape matching using low distortion correspondences between feature points. The core of our method is an innovative algorithm that formulates the correspondence problem as an integer quadratic programming task, integrating both the similarity of corresponding geometric blur point descriptors and the geometric distortion between pairs of feature points into the cost function. This approach is robust to outliers, making it effective for matching exemplars to query images even in scenarios with occlusion and clutter. Once correspondences are established, we apply a regularized thin plate spline as an aligning transform, leading to a dense correspondence between shapes. For object recognition, we adopt a nearest neighbor framework where the distance between an exemplar and a query is determined by the matching cost between corresponding points. Our method is validated on two datasets. Firstly, we demonstrate its efficacy on the highly challenging Caltech 101 dataset, achieving a 48% correct classification rate, which is significantly higher than the 16% reported by Fei-Fei et al. Secondly, we showcase comparable results for localizing frontal and profile faces, rivaling specialized face-tuned approaches.

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

RECENTLY , several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches. Firstly, this dissertation will present a new framework for multi-target tracking in a single camera. We proposed a novel data association technique using Dominant set clustering (DCS) framework. We formulate the tracking task as finding dominant sets on the constructed undirected edge weighted graph. Unlike most techniques which are limited in temporal locality (i.e. few frames are considered), we utilized a pairwise relationships (in appearance and position) between different detections across the whole temporal span of the video for data association in a global manner. Meanwhile, temporal sliding window technique is utilized to find tracklets and perform further merging on them. Our robust tracklet merging step renders our tracker to long term occlusions with more robustness. DSC leads us to a more accurate approach to multi-object tracking by considering all the pairwise relationships in a batch of frames; however, it has some limitations. Firstly, it finds target trajectories(clusters) one-by-one (peel off strategy), causing change in the scale of the problem. Secondly, the algorithm used to enumerate dominant sets, i.e., replicator dynamics, have quadratic computational complexity, which makes it impractical on larger graphs. To address these problems and extend tracking problem to multiple non-overlapping cameras, we proposed a novel and unified three-layer hierarchical approach. Given a video and a set of detections (obtained by any person detector), we first solve withincamera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant set clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as

In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.

Pedestrian detection is a critical issue in computer vision, with several feature descriptors can be adopted. Since the ability of various kinds of feature descriptor is different in pedestrian detection and there is no basis in feature selection, we analyze the commonly used features in theory and compare them in experiments. It is desired to find a new feature with the strongest description ability from their pair-wise combinations. In experiments, INRIA database and Daimler database are adopted as the training and testing set. By theoretic analysis, we find the HOG–LSS combined feature have more comprehensive description ability. At first, Adaboost is regarded as classifier and the experimental results show that the description ability of the new combination features is improved on the basis of the single feature and HOG–LSS combined feature has the strongest description ability. For further verifying this conclusion, SVM classifier is used in the experiment. The detection performance is evaluated by miss rate, the false positives per window, and the false positives per image. The results of these indicators further prove that description ability of HOG–LSS feature is better than other combination of these

Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency. An extended Kalman filter (EKF) is used to estimate the position and velocity of the bicycle in vehicle coordinates. We demonstrate the effectiveness of this approach through a series of experiments run on video data of moving bicycles captured from a vehicle-mounted camera

A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available at lsda.berkeleyvision.org.

We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each superpixel and then refine our results further by using the classifier in a conditional random field operating on the superpixel graph. Our proposed method exceeds the previously published state-of-the-art on two challenging datasets: Graz-02 and the PASCAL VOC 2007 Segmentation Challenge.

Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which “support” the particular function. Our novel approach “imagines” an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category “sitting”. Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.

Object detection has over the past few years converged on using linear SVMs over HOG features. Training linear SVMs however is quite expensive, and can become intractable as the number of categories increase. In this work we revisit a much older technique, viz. Linear Discriminant Analysis, and show that LDA models can be trained almost trivially, and with little or no loss in performance. The covariance matrices we estimate capture properties of natural images. Whitening HOG features with these covariances thus removes naturally occuring correlations between the HOG features. We show that these whitened features (which we call WHO) are considerably better than the original HOG features for computing similarities, and prove their usefulness in clustering. Finally, we use our findings to produce an object detection system that is competitive on PASCAL VOC 2007 while being considerably easier to train and test.


Question: An ensemble approach using exemplar-SVMs for object detection and other applications.

This paper explores the Pascal Visual Object Classes (VOC) Challenge, an established benchmark for visual object category recognition and detection. Since its inception in 2005, the challenge has offered a standardized dataset of images, annotations, and evaluation protocols to the vision and machine learning communities, becoming a cornerstone for evaluating object detection algorithms. The paper outlines the dataset and the evaluation process, providing an in-depth analysis of the state-of-the-art methodologies for classification and detection. It assesses the statistical differences between these methods, their ability to discern objects from their context, and their performance in handling various complexities. The review concludes by summarizing the key insights gained over the three-year duration of the challenge, suggesting potential avenues for enhancement and expansion. Through this analysis, the paper underscores the evolving landscape of object detection and the role of the Pascal VOC Challenge in advancing the field.

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.

A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision. This paper has been published in Artificial Intelligence, 31, 3 (March 1987), pp. 355–395.

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.

In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.

We describe a two-stage method for detecting hands and their orientation in unconstrained images. The first stage uses three complementary detectors to propose hand bounding boxes. Each bounding box is then scored by the three detectors independently, and a second stage classifier learnt to compute a final confidence score for the proposals using these features. We make the following contributions: (i) we add context-based and skin-based proposals to a sliding window shape based detector to increase recall; (ii) we develop a new method of non-maximum suppression based on super-pixels; and (iii) we introduce a fully annotated hand dataset for training and testing. We show that the hand detector exceeds the state of the art on two public datasets, including the PASCAL VOC 2010 human layout challenge.

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.

Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency. An extended Kalman filter (EKF) is used to estimate the position and velocity of the bicycle in vehicle coordinates. We demonstrate the effectiveness of this approach through a series of experiments run on video data of moving bicycles captured from a vehicle-mounted camera


Question: An ensemble method using exemplar-SVMs for object detection and other applications.

This paper delves into the exploration of feature sets for achieving robust visual object recognition, utilizing linear SVM based human detection as a practical scenario. Building upon previous work in edge and gradient descriptors, we present empirical evidence that grids of Histograms of Oriented Gradients (HOG) descriptors outperform conventional feature sets significantly in human detection tasks. A detailed analysis of each stage in the computation process reveals that fine-scale gradients, precise orientation binning, relatively coarse spatial binning, and superior local contrast normalization within overlapping descriptor blocks are critical for optimal performance. Our novel approach yields near-perfect classification on the initial MIT pedestrian dataset. To further challenge and validate our methodology, we introduce a more sophisticated dataset comprising over 1800 annotated human images, encompassing a wide spectrum of poses and diverse backgrounds. This enhanced dataset allows for a more rigorous evaluation of the HOG descriptor's efficacy in complex and varied scenarios, showcasing its robustness and adaptability in real-world object recognition tasks.

Learning to recognize of object classes is one of the most important functionalities of vision. It is estimated that humans are able to learn tens of thousands of visual categori es in their life. Given the photometric and geometric variabilities displayed by objects as well as the high degree of intra-clas s variabilities, we hypothesize that humans achieve such a fe at by using knowledge and information cumulated throughout the learning process. In recent years, a handful of pioneering p apers have applied various forms of knowledge transfer algorithms to the problem of learning object classes. We first review some o f these papers by loosely grouping them into three categories : transfer through prior parameters, transfer through shared features or parts, and transfer through contextual information. In the second half of the paper, we detail a recent algorithm proposed by the author. This incremental learning scheme us es information from object classes previously learned in the f orm of prior models to train a new object class model. Training images can be presented in an incremental way. We present experimental results tested with this model on a large numbe r of object categories.

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.

In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.

We propose a new method for the task of fine-grained visual categorization. The method builds a model of the base-level category that can be fitted to images, producing high-quality foreground segmentation and mid-level part localizations. The model can be learnt from the typical datasets available for fine-grained categorization, where the only annotation provided is a loose bounding box around the instance (e.g. bird) in each image. Both segmentation and part localizations are then used to encode the image content into a highly-discriminative visual signature. The model is symbiotic in that part discovery/localization is helped by segmentation and, conversely, the segmentation is helped by the detection (e.g. part layout). Our model builds on top of the part-based object category detector of Felzenszwalb et al., and also on the powerful Grab Cut segmentation algorithm of Rother et al., and adds a simple spatial saliency coupling between them. In our evaluation, the model improves the categorization accuracy over the state-of-the-art. It also improves over what can be achieved with an analogous system that runs segmentation and part-localization independently.

We propose a method to identify and localize object classes in images. Instead of operating at the pixel level, we advocate the use of superpixels as the basic unit of a class segmentation or pixel localization scheme. To this end, we construct a classifier on the histogram of local features found in each superpixel. We regularize this classifier by aggregating histograms in the neighborhood of each superpixel and then refine our results further by using the classifier in a conditional random field operating on the superpixel graph. Our proposed method exceeds the previously published state-of-the-art on two challenging datasets: Graz-02 and the PASCAL VOC 2007 Segmentation Challenge.

This paper presents a method for pedestrian detection and tracking using a single night-vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road-detection module that provides key information for pedestrian validation. Experimental comparisons (e.g., grayscale SVM recognition versus binary SVM recognition and entire-body detection versus upper-body detection) have been carried out to illustrate the feasibility of our approach.


Question: An ensemble of exemplar-SVMs for object recognition and beyond.

The SUN Database: A Comprehensive Resource for Scene Recognition from Abbey to Zoo

The challenge of scene categorization is pivotal in the field of computer vision, yet progress in this area has been limited by the narrow scope of existing databases that fail to encompass the full spectrum of scene categories. In contrast to object categorization datasets, which often feature hundreds of distinct object classes, the largest scene category dataset has historically been limited to a mere 15 classes. This paper introduces the Scene UNderstanding (SUN) database, a vast collection designed to address this disparity. The SUN database comprises 899 diverse scene categories and an impressive 130,519 images. Using a subset of 397 well-represented categories, we conduct an extensive evaluation of state-of-the-art scene recognition algorithms, setting new benchmarks for performance. Our analysis also includes a comparison between human and computational scene classification capabilities on the SUN database. Furthermore, we delve into a more detailed scene representation, exploring methods to identify scenes within larger, more complex scenes. This comprehensive approach underscores the SUN database's potential to significantly advance the field of scene understanding and recognition.

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.

Dual decomposition methods are the current state-of-the-art for training multiclass formulations of Support Vector Machines (SVMs). At every iteration, dual decomposition methods update a small subset of dual variables by solving a restricted optimization problem. In this paper, we propose an exact and efficient method for solving the restricted problem. In our method, the restricted problem is reduced to the well-known problem of Euclidean projection onto the positive simplex, which we can solve exactly in expected O(k) time, where k is the number of classes. We demonstrate that our method empirically achieves state-of-the-art convergence on several large-scale high-dimensional datasets.

This course will provide an introduction to statistical pattern recognition. The lectures will focus on different techniques including methods for feature extraction, dimensionality reduction, data clustering and pattern classification. State-of-art approaches such as ensemble learning and sparse modelling will be introduced. Selected real-world applications will illustrate how the techniques are applied in practice.

Outlier detection is an unsupervised problem, in which labels are not available with data records [2]. As a result, it is generally more challenging to design ensemble analysis algorithms for outlier detection. In particular, methods that require the use of labels in intermediate steps of the algorithm cannot be generalized to outlier detection. For example, in the case of boosting, the classifier algorithm needs to be evaluated in the intermediate steps of the algorithm with the use of training-data labels. Such methods are generally not possible in the case of outlier analysis. As discussed in [1], there are unique reasons why ensemble analysis is generally more difficult in the case of outlier analysis as compared to classification. In spite of the unsupervised nature of outlier ensemble analysis, we show that the theoretical foundations of outlier analysis and classification are surprisingly similar. A number of useful discussions on the theory of classification ensembles may be found in [27, 29, 33]. Further explanations on the use of the bias-variance decomposition in different types of classifiers such as neural networks, support vector machines, and rule-based classifiers are discussed in [17, 30, 31]. It is noteworthy that the biasvariance decomposition is often used in customized ways for different types of base classifiers and combination methods; this general principle is also true in outlier detection. Several arguments have recently been proposed on the theory explaining the accuracy improvements of outlier ensembles. In some cases, incorrect new arguments (such as those in [32]) are proposed to justify experimental results that can be explained by well-known ideas, and an artificial distinction is made between the theory of classification ensembles and outlier ensembles. A recent paper [4]

Nowadays computer vision technology is helping the visually impaired by recognizing objects in their surroundings. Unlike research of navigation and wayfinding, there are no camera-based systems available in the market to find personal items for the blind. This paper proposes an object recognition method to help blind people find missing items using Speeded-Up Robust Features (SURF). SURF features can extract distinctive invariant features that can be utilized to perform reliable matching between different images in multiple scenarios. These features are invariant to image scale, translation, rotation, illumination, and partial occlusion. The proposed recognition process begins by matching individual features of the user queried object to a database of features with different personal items which are saved in advance. Experiment results demonstrate the effectiveness and efficiency of the proposed method.

In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications.

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

Font Recognition is one of the Challenging tasks in Optical Character Recognition. Most of the existing methods for font recognition make use of local typographical features and connected component analysis. In this paper, English font recognition is done based on global texture analysis. The main objective of this proposal is to employ support vector machines (SVM) in identifying various fonts. The feature vectors are extracted by making use of Gabor filters and the proposed SVM is trained using these features. The method is found to give superior performance over neural networks by avoiding local minima points. The SVM model is formulated tested and the results are presented in this paper. It is observed that this method is content independent and the SVM classifier shows an average accuracy of 93.54%.


Question: An ensemble of exemplar-SVMs for object recognition and more.

This research paper introduces an innovative method for shape detection and object recognition, utilizing a distinctive feature known as shape contexts. Shape contexts provide a comprehensive description of an object's boundary points relative to each other, capturing the intrinsic geometry of the shape in a manner that is robust against fundamental transformations such as translation, scaling, and rotation. By leveraging this feature, the paper demonstrates an effective approach to object recognition that involves comparing the computed shape context of unknown objects against a database of known shape contexts. This technique has been successfully applied to various datasets, including handwritten digits and trademark images, yielding promising results that underscore its potential in the field of object recognition. The computational efficiency and invariance properties of this method make it a valuable tool for applications requiring fast and accurate shape matching.

While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).

We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labelled points. An initial estimate of the location of the model points in an image is improved by attempting to move each point to a better position nearby. Adjustments to the pose variables and shape parameters are calculated. Limits are placed on the shape parameters ensuring that the example can only deform into shapes conforming to global constraints imposed by the training set. An iterative procedure deforms the model example to find the best fit to the image object. Results of applying the method are described. The technique is shown to be a powerful method for refining estimates of object shape and location.

We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SVM based on identifying “outlier” data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called “Hadamard” representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on “bottleneck” compression generated filters. The SVM approach as represented by Schölkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.

In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.

The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.

Faces represent complex, multidimensional, meaningful visual stimuli an d developing a computational model for face recognition is difficult [42]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sam pling, a self-organizing map neural network, and a convolutional neural network. The self-organizi ng map provides a quantization of the image samples into a topological space where inputs that are n e rby in the original space are also nearby in the output space, thereby providing dimensionality red uction and invariance to minor changes in the image sample, and the convolutional neural network prov ides for partial invariance to translation, rotation, scale, and deformation. The convolutional net work extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen -Loève transform in place of the self-organizing map, and a multi-layer perceptron in place of the con volutional network. The Karhunen-Loève transform performs almost as well (5.3% error versus 3 .8%). The multi-layer perceptron performs very poorly (40% error versus 3.8%). The method is cap able of rapid classification, requires only fast, approximate normalization and preprocessing, and cons istently exhibits better classification performance than the eigenfaces approach [42] on the database consider ed as the number of images per person in the training database is varied from 1 to 5. With 5 imag es per person the proposed method and eigenfaces result in 3.8% and 10.5% error respectively. The recogni zer provides a measure of confidence in its output and classification error approaches zero when rejecting as few as 10% of the examples. We use a database of 400 images of 40 individuals which cont ains quite a high degree of variability in expression, pose, and facial details. We analyze computati onal complexity and discuss how new classes could be added to the trained recognizer.

We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class's training descriptors, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. Local NBNN gives a 100 times speed-up over the original NBNN on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features. We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model. However, we find that local NBNN, while competitive with, does not beat state-of-the-art spatial pyramid methods that use local soft assignment and max-pooling.


Question: A revised timeboxing process model for effective resource utilization.

THE EXPERTISE WORKSHOP: A FRAMEWORK FOR OPTIMIZING LIFE CYCLE EXPERIENCE AND ASSET REUTILIZATION

This paper introduces the Expertise Workshop, a comprehensive infrastructure designed to maximize the capitalization and reutilization of life cycle experience and assets. The Expertise Workshop operates as both a logical and physical entity, functioning independently of the development organization. The roles and activities of the Expertise Workshop are distinct from those of the development organization, yet they complement each other in the following manner:

1. **Experience Accumulation**: The Expertise Workshop gathers and archives experience and assets from the life cycle of products and services. This includes documentation, methodologies, tools, and best practices.

2. **Expertise Curation**: The Workshop curates the collected experience and assets, ensuring they are organized, up-to-date, and accessible. This involves the maintenance of a knowledge base that can be easily searched and referenced.

3. **Resource Allocation**: The Workshop optimizes the allocation of resources by identifying which assets and experiences are most valuable and can be reused or repurposed. This process ensures that resources are directed to areas where they can have the greatest impact.

4. **Training and Development**: The Expertise Workshop provides training to the development organization, equipping members with the skills and knowledge necessary to effectively utilize the resources available. This includes workshops, seminars, and mentoring programs.

5. **Innovation and Improvement**: By analyzing past experiences and assets, the Workshop facilitates innovation and continuous improvement in the development process. It identifies areas for enhancement and proposes new methodologies or tools to streamline operations.

6. **Collaboration and Networking**: The Expertise Workshop fosters a collaborative environment where knowledge and experience can be shared among team members and across different projects. This promotes a culture of learning and innovation.

7. **Performance Assessment**: The Workshop assesses the performance of the development organization, providing feedback and recommendations for improvement. This includes measuring the effectiveness of resource utilization and the impact of experience and asset reutilization on project outcomes.

By establishing the Expertise Workshop, organizations can ensure that life cycle experience and assets are not only preserved but also leveraged to enhance future projects, leading to increased efficiency, reduced costs, and improved product quality.

We consider the problem of appointment scheduling in a robus t optimization framework. The appointment scheduling problem arises in many service operations, for e xample health care. For each job, we are given its minimum and maximum possible execution times. The objective is to find an appointment schedule for which the cost in the worst case scenario of the realization of the processi ng times of the jobs is minimized. We present a global balancing heuristic, which gives an easy to compute closed f orm optimal schedule when the underage costs of the jobs are non-decreasing. In addition, for the case where we h av the flexibility of changing the order of execution of the jobs, we give simple heuristics to find a near-optimal seq uence of the jobs. Working paper, Operations Research Center, MIT. Last updat ed March 3, 2012. Amazon.com, 333 Boren Avenue N, Seattle WA 98101, USA. Email : mshashi@alum.mit.edu. TU Berlin, Matheon, Berlin, Germany. Email: sebastian.stiller@tu-berlin.de.

This paper reports results from a survey of experienced project managers on their perception of software risk management. From a sample of 18 experienced project managers, we have found good awareness of risk management, but low tool usage. We offer evidence that the main barriers to performing risk management are related to its perceived high cost and comparative low value. Psychological issues are also important, but less so. Risk identification and monitoring, in particular, are perceived as effort intensive and costly. The perception is that risk management is not prioritised highly enough. Our conclusion is that more must be done to visibly prove the value : cost ratio for risk management activities.

The games industry is notorious for its intense work ethics with uncompensated overtime and weekends at the office, also known as crunch or crunch time. Since crunch time is so common within the industry, is it possible that the benefits of crunch time outweigh the disadvantages? By studying postmortems and conducting interviews with employees in the industry, we aim to characterise crunch time and discover its effects on the industry. We provide a classification of crunch, i.e., four types of crunch which all have distinct characteristics and affect the product, employees and schedule in various ways. One of the crunch types stands out from the others by only having positive effects on product and schedule. A characteristic that all of the types have in common is an increase in stress levels amongst the employees. We identify a set of reasons for crunch and show that crunch is less pronounced in game studios where prioritisation of features is a regular practice.

Software Product Lines (SPL) and Agile practices have emerged as new paradigms for developing software. Both approaches share common goals; such as improving productivity, reducing time to market, decreasing development costs and increasing customer satisfaction. These common goals provide the motivation for this research. We believe that integrating Agile practices into SPL can bring a balance between agility and formalism. However, there has been little research on such integration. We have been researching the potential of integrating Agile approaches in one of the key SPL process areas, product derivation. In this paper we present an outline of our Agile process model for product derivation that was developed through industry based case study research.

It has been suggested that there is more than one reason for a software development project to fail. However, most of the literature that discusses project failure tends to be rather general, supplying us with lists of risk and failure factors, and focusing on the negative business effects of the failure. Very little research has attempted an in-depth investigation of a number of failed projects to identify exactly what are the factors behind the failure. In this research we analyze data from 70 failed projects. This data provides us with practitionerspsila perspectives on 57 development and management factors for projects they considered were failures. Our results show that all projects we investigated suffered from numerous failure factors. For a single project the number of such factors ranges from 5 to 47. While there does not appear to be any overarching set of failure factors we discovered that all of the projects suffered from poor project management. Most projects additionally suffered from organizational factors outside the project managerpsilas control. We conclude with suggestions for minimizing the four most common failure factors.

A description is given of a software-process maturity framework that has been developed to provide the US Department of Defense with a means to characterize the capabilities of software-development organizations. This software-development process-maturity model reasonably represents the actual ways in which software-development organizations improve. It provides a framework for assessing these organizations and identifying the priority areas for immediate improvement. It also helps identify those places where advanced technology can be most valuable in improving the software-development process. The framework can be used by any software organization to assess its own capabilities and identify the most important areas for improvement.<<ETX>>

Business Process Reengineering increases enterprise's chance to survive in competition among organizations , but failure rate among reengineering efforts is high, so new methods that decrease failure, are needed, in this paper a business process reengineering method is presented that uses Enterprise Ontology for modelling the current system and its goal is to improve analysing current system and decreasing failure rate of BPR, and cost and time of performing processes, In this method instead of just modelling processes, processes with their : interactions and relations, environment, staffs and customers will be modelled in enterprise ontology. Also in choosing processes for reengineering step, after choosing them, processes which, according to the enterprise ontology, has the most connection with the chosen ones, will also be chosen to reengineer, finally this method is implemented on a company and As-Is and To-Be processes are simulated and compared by ARIS tools, Report and Simulation Experiment

Local Process Models (LPMs) describe structured fragments of process behavior occurring in the context of less structured business processes. In contrast to traditional support-based LPM discovery, which aims to generate a collection of process models that describe highly frequent behavior, High-Utility Local Process Model (HU-LPM) discovery aims to generate a collection of process models that provide useful business insights by specifying a utility function. Mining LPMs is a computationally expensive task, because of the large search space of LPMs. In supportbased LPM mining, the search space is constrained by making use of the property that support is anti-monotonic. We show that in general, we cannot assume a provided utility function to be anti-monotonic, therefore, the search space of HU-LPMs cannot be reduced without loss. We propose four heuristic methods to speed up the mining of HU-LPMs while still being able to discover useful HU-LPMs. We demonstrate their applicability on three real-life data sets.

IEEE 802.11e HCCA reference scheduler guarantees Quality of Service only for Constant Bit Rate traffic streams, whereas its assignment of scheduling parameters (transmission time TXOP and polling period) is too rigid to serve Variable Bit Rate (VBR) traffic.
 This paper presents a new scheduling algorithm, Dynamic TXOP HCCA (DTH). Its scheduling scheme, integrated with the centralized scheduler, uses both a statistical estimation of needed transmission duration and a bandwidth reclaiming mechanism with the aim of improving the resource management and providing an instantaneous dynamic Transmission Opportunity (TXOP), tailored to multimedia applications with variable bit rate. Performance evaluation through simulation, confirmed by the scheduling analysis, shows that DTH is suitable to reduce the transmission queues length. This positively impacts on the delay and on packets drop rate experienced by VBR traffic streams.

We present an extension of the well-known time-expanded approach for timetable information. By remodeling unimportant stations, we are able to obtain faster query times with less space consumption than the original model. Moreover, we show that our extensions harmonize well with speed-up techniques whose adaption to timetable networks is more challenging than one might expect.


Question: An adapted timeboxing process model for effective resource utilization.

This paper introduces an enhanced version of the Timeboxing process model, specifically designed to streamline iterative software development and optimize resource utilization. In today's fast-paced business environment, where velocity is crucial, an iterative development methodology that enables the incremental delivery of functionality has become indispensable for managing risks effectively. The proposed model builds upon the foundational concept of time-boxed iterations, where each iteration is of a fixed duration, and the scope of work is adjusted to fit within this timeframe. Unlike traditional sequential execution of iterations, the enhanced Timeboxing process model incorporates pipelining concepts, allowing for concurrent execution of different iterations, thereby significantly reducing the overall delivery time for product releases. The model achieves this by dividing each time-boxed iteration into equally sized stages, each with a specific function and a clear deliverable that is passed on to the subsequent stage. This staged approach facilitates the concurrent execution of multiple time boxes, improving the efficiency of the development process. To demonstrate the practical application and success of the model, a real-world commercial project that was efficiently executed using this enhanced Timeboxing process is presented as a case study.

Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

We describe a variety of inter-related decisions made during daily operations at a container terminal. The ultimate goal of these decisions is to minimize the berthing time of vessels, the resources needed for handling the workload, the waiting time of customer trucks, and the congestion on the roads and at the storage blocks and docks inside the terminal; and to make the best use of the storage space. Given the scale and complexity of these decisions, it is essential to use decision support tools to make them. This paper reports on work to develop such a decision support system (DSS). We discuss the mathematical models and algorithms used in designing the DSS, the reasons for using these approaches, and some experimental results. D 2003 Elsevier B.V. All rights reserved.

With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers’ purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan’s industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.

Hard real-time embedded systems employ high-capacity memories such as Dynamic RAMs (DRAMs) to cope with increasing data and code sizes of modern designs. However, memory controller design has so far largely focused on improving average-case performance. As a consequence, the latency of memory accesses is unpredictable, which complicates the worst-case execution time analysis necessary for hard real-time embedded systems.
 Our work introduces a novel DRAM controller design that is predictable and that significantly reduces worst-case access latencies. Instead of viewing the DRAM device as one resource that can only be shared as a whole, our approach views it as multiple resources that can be shared between one or more clients individually. We partition the physical address space following the internal structure of the DRAM device, i.e., its ranks and banks, and interleave ac- cesses to the blocks of this partition. This eliminates contention for shared resources within the device, making accesses temporally predictable and temporally isolated. This paper describes our DRAM controller design and its integration with a precision-timed (PRET) architecture called PTARM. We present analytical bounds on the latency and throughput of the proposed controller, and confirm these via simulation.

Although agile software development methods such as SCRUM and DSDM are gaining popularity, the consequences of applying agile principles to software product management have received little attention until now. In this paper, this gap is filled by the introduction of a method for the application of SCRUM principles to software product management. For this purpose, the 'agile requirements refinery' is presented, an extension to the SCRUM process that enables product managers to cope with large requirements in an agile development environment. A real-life case study is presented to illustrate how agile methods can be applied to software product management. The experiences of the case study company are provided as a set of lessons learned that will help others to apply agile principles to their software product management process.

|As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more di cult and errorprone. This paper discusses programming assistance and automation concepts and their application to a program development tool for message-passing systems called Hypertool. It performs scheduling and handles the communication primitive insertion automatically. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs.

Fully automated 300mm manufacturing requires the adoption of a real-time lot dispatching paradigm. Automated dispatching has provided significant improvements over manual dispatching by removing variability from the thousands of dispatching decisions made every day in a fab. Real-time resolution of tool queues, with consideration of changing equipment states, process restrictions, physical and logical location of WIP, supply chain objectives and a myriad of other parameters, is required to ensure successful dispatching in the dynamic fab environment. However, the real-time dispatching decision in semiconductor manufacturing generally remains a reactive, heuristic response in existing applications, limited to the current queue of each tool. The shortcomings of this method of assigning WIP to tools, aptly named "opportunistic scavenging" as stated in G. Sullivan (1987), have become more apparent in lean manufacturing environments where lower WIP levels present fewer obvious opportunities for beneficial lot sequencing or batching. Recent advancements in mixed integer programming (MIP) and constraint programming (CP) have raised the possibility of integrating optimization software, commonly used outside of the fab environment to compute optimal solutions for scheduling scenarios ranging from order fulfillment systems to crew-shift-equipment assignments, with a real-time dispatcher to create a short-interval scheduler. The goal of such a scheduler is to optimize WIP flow through various sectors of the fab by expanding the analysis beyond the current WIP queue to consider upstream and downstream flow across the entire tool group or sector. This article describes the production implementation of a short-interval local area scheduler in IBM's leading-edge 300mm fab located in East Fishkill, New York, including motivation, approach, and initial results

There is widespread need for safe, verifiable, efficient, and reliable software that can be delivered in a timely manner. Software reuse can make a valuable contrbution toward this goal by increasing programmer productivity and software quality. Unfortunately, the amount of software reuse currently done is quite small. DeMarco [1] estimates that in the average software development environment only about five percent of code is reused.

Software evolution is widely recognized as an important and common phenomenon, whereby the system follows an ever-extending development trajectory w ith intermittent releases. Nevertheless there have been only few lifecycle models that attempt to por tray such evolution. We use the evolution of the Linux kernel as the basis for the formulation of such a model, integrating the progress in time with growth of the codebase, and differentiating bet ween development of new functionality and maintenance of production versions. A unique elem nt of the model is the sequence of activities involved in releasing new production versions, and how this has changed with the growth of Linux. In particular, the release follow-up phase before th forking of a new development version, which was prominent in early releases of production ve rsions, has been eliminated in favor of a concurrent merge window in the release of 2.6.x versions . We also show that a piecewise linear model with increasing slopes provides the best descr iption of the growth of Linux. The perpetual development model is used as a framework in which c ommonly recognized benefits of incremental and evolutionary development may be demonstra ted, nd to comment on issues such as architecture, conservation of familiarity, and failed p rojects. We suggest that this model and variants thereof may apply to many other projects in additio n to Linux.

Software metric is a measure of some property of a piece of software or its specifications. The goal is to obtain reproducible and quantifiable measurements, which may have several valuable applications in schedule and budget planning, effort and cost evaluation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Software effort evaluation is one of the most essential and crucial part of software project planning for which efficient effort metrics is required. Software effort evaluation is followed by software cost evaluation which is helpful for both customers and developers. Thus, efficiency of effort component of software is very essential. The algorithmic models are weak in estimating early effort evaluation with regards to uncertainty and imprecision in software projects. To overcome this problem, there are various machine learning methods. One of the methods is soft computing in which there are various methodologies viz., Artificial Neural Network, Fuzzy Logic, Evolutionary computation based Genetic Algorithm and Metaheuristic based Particle Swarm Optimization. These methods are good at solving real-world ambiguities. This paper highlights the design of an efficient software effort evaluation model using Adaptive Neuro-Fuzzy Inference System (ANFIS) for uncertain datasets and it shows that this technique significantly outperforms with sufficient results.