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检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

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Performance Evaluation of vision-Based High DOF Human Movement Tracking: A Survey And Human computer Interaction Perspective

Performance Evaluation of Vision-Based High DOF Human Moveme...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： D. Heckenberg School of Computer Science and Engineering University of New South Wales Australia

Accurate tracking of human movement has the potential to enable many kinds of human-computer interaction (HCI). vision-based methods promise tracking without encumberance by body-mounted apparatus but constitute a significant research challenge. Its many applications both in HCI and other areas such as surveillance have made markerless human tracking a central problem in computer vision. Despite this research focus, there are few established mechanisms for evaluating and comparing the performance of reported solutions. Furthermore, very few systems have attempted human-centered evaluation using methods from HCI research. This paper presents a review of evaluation methods employed in the literature of high degree of freedom human movement tracking systems. Detailed full-body tracking and hand tracking systems are included. Suitable evaluation methods employed in other computer vision and HCI research are also considered.

关键词： Tracking Human computer interaction computer vision Application software Surveillance Real time systems Optical sensors Feedback computer science Australia

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Statistical Analysis of Local 3D Structure in 2D Images

Statistical Analysis of Local 3D Structure in 2D Images

引用

conference on computer vision and pattern recognition (CVPR)

作者： S. Kalkan F. Worgotter N. Kruger Bernstein Centre for Computational Neuroscience University of Göttingen Germany Cognitive Vision Group Aalborg University Copenhagen Denmark

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Multi-modal Scene Reconstruction using Perceptual Grouping Constraints

Multi-modal Scene Reconstruction using Perceptual Grouping C...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： N. Pugeault F. Worgotter N. Kruger University of Edinburgh UK University of Göttingen Germany Aalborg University Copenhagen Denmark

In this work we propose a scheme integrating perceptual grouping into stereopsis to reduce the ambiguity of those early processes. We propose a simple perceptual grouping algorithm that - in addition to the geometric information makes use of a novel multi-modal affinity measure between local primitives. We then use this group information to 1) disambiguate the stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. We show quantitative and qualitative demonstrations of those processes on a variety of sequences.

关键词： Layout Image reconstruction Stereo vision Data mining Stereo image processing Interpolation Fluid flow measurement Phase measurement Testing Error correction

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Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification

Generalized Multiclass AdaBoost and Its Applications to Mult...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Hao Jiebo Luo Kodak Research Laboratories Eastman Kodak (Japan) Limited USA

AdaBoost has received considerable attention in the vision and multimedia research community in recent years. It is originally designed for two-class classification problems. To handle multiple classes, many AdaBoost extensions have been developed primarily based on various schemes for reducing multiclass classification to multiple two-class problems. From a statistical prospective, AdaBoost can be viewed as a forward stepwise additive model using an exponential loss function. In this paper, we derive a generalized form of AdaBoost for multiclass classification based on a multiclass exponential loss function. To prove its effectiveness, we benchmarked a number of multimedia problems of different nature. Experimental results show that the new boosting algorithm outperforms other multiclass alternatives. In addition, the generalized boosting algorithm can be used to either boost a multiclass classifier, or build a multiclass classifier from a binary one.

关键词： Boosting Classification algorithms Machine learning algorithms Machine learning Layout Training data computer vision pattern recognition conferences Testing

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Perceptual Information of Images and the Bias in Homogeneity-based Segmentation

Perceptual Information of Images and the Bias in Homogeneity...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hui Zhang S.A. Goldman Department of Computer Science and Engineering Washington University Saint Louis MO USA

Semantic image segmentation aims to partition an image into separate regions, which ideally corresponds to different real-world objects. Many segmentation algorithms have been proposed, exploiting a wide variety of image features and characteristics. It has been shown through empirical studies that segmentation methods that assume a good segmentation partitions an image into different homogeneous regions are likely to fail in non-trivial situations, while methods based on perceptual organization generally generate more favorable segmentations. Yet no formal justification has been provided. In this paper, we propose an information measure for images, the perceptual information, based on human visual perception organization. Using perceptual information, we justify that homogeneity-based segmentation methods are inherently biased, and by incorporating knowledge, perceptual organization can overcome the bias and generate better segmentations.

关键词： Image segmentation Humans Visual perception Partitioning algorithms computer vision computer science Drives Application software Image analysis Object recognition

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Kernel-based recognition of Human Actions Using Spatiotemporal Salient Points

Kernel-based Recognition of Human Actions Using Spatiotempor...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： A. Oikonomopoulos I. Patras M. Pantic Computing Department Imperial College London UK Computer Vision and Pattern Recognition Group University of York UK

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises.

关键词： Humans Spatiotemporal phenomena pattern recognition computer vision Image recognition Image sequences Detectors Entropy Image retrieval Educational institutions

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Learning to classify by ongoing feature selection

Learning to classify by ongoing feature selection

引用

3rd Canadian conference on computer and Robot vision, CRV 2006

作者： Levi, Dan Ullman, Shimon Weizmann Institute of Science Department of Computer Science and Mathematics Rehovot 76100 Israel

ISBN: (纸本)0769525423

Existing classification algorithms use a set of training examples to select classification features, which are then used for all future applications of the classifier. A major problem with this approach is the selection of a training set: A small set will result in reduced performance, and a large set will require extensive training. In addition, class appearance may change over time requiring an adaptive classification system. In this paper we propose a solution to these basic problems by developing an on-line feature selection method, which continuously modifies and improves the features used for classification based on the examples provided so far. The method is used for learning a new class, and to continuously improve classification performance as new data becomes available. In ongoing learning, examples are continuously presented to the system, and new features arise from these examples. The method continuously measures the value of the selected features using mutual information, and uses these values to efficiently update the set of selected features when new training information becomes available. The problem is challenging because at each stage the training process uses a small subset of the training data. Surprisingly, with sufficient training data the on-line process reaches the same performance as a scheme that has a complete access to the entire training data. © 2006 ieee.

关键词： Feature extraction

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An adaptive clustering for multiple object tracking in sequences in and beyond the visible spectrum

An adaptive clustering for multiple object tracking in seque...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： S. Dubuisson Laboratoire dInformatique de Paris LIP6 UPMC Paris France

In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.

关键词： Gabor filters Filter bank Target tracking Solid modeling Filtering computer vision Feature extraction Cameras Adaptive filters Robustness

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Ensemble Method for Robust Motion Estimation

Ensemble Method for Robust Motion Estimation

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Wei Zhang J. Kosecka George Mason University Fairfax VA USA

The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric ensemble techniques, we demonstrate that it proves advantageous to use the entire set of hypotheses generated in the sampling stage. We show that by studying the residual distribution of each data point with respect to the entire set of hypotheses, the problem of inlier/ outlier identification can be formulated as a classification problem. We present extensive simulations of the approach, which in the presence of a large percentage (> 50%) of outliers, provides a repeatable and, an order of magnitude more efficient method compared to the currently existing techniques. Results on widebaseline matching and fundamental matrix estimation are presented.

关键词： Robustness Motion estimation Sampling methods computer vision Testing Parameter estimation Context modeling Maximum likelihood estimation Iterative algorithms Iterative methods

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Simple software for microarray image analysis

Simple software for microarray image analysis

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3rd Canadian conference on computer and Robot vision, CRV 2006

作者： Chen, Chaur-Chin Kao, Cheng-Yan Chang, Chun-Fan Chu, Hsueh-Ting Chen, Chiung-Nien Department of Computer Science National Tsing Hua University Hsinchu 30013 Taiwan Angiogenesis Research Center National Taiwan University Hospital Taipei 106 Taiwan

ISBN: (纸本)0769525423

A set of microarray images were acquired by a sequence of biological experiments which were scanned via a high resolution scanner. For each spot corresponding to a gene, the ratio of Cy3 and Cy5 fluorescent signal intensities was obtained and which may be normalied based on piecewise linear regression such as lowess method. In this study, we computed from 55 microarray images to get an M × N genematrix, A, with N = 55 patients and M = 13574 effected genes in each microarray. We start with our gene discovery from a genematrix A ∈ RM×N, M = 13574, N = 55, including N1 = 29 patients of hepatitis B virus (HBV), N2 = 21 patients of hepatitis C virus (HCV), 1 patient clinically diagnosed to be infected with HCV as well as HBV, and 4 patients were neither HCV nor HBV infected. Simple software was developed to solve the following problems: (i) Detect differentially expressed genes and (ii) Select a subset of genes which best distinguishes HBV patients from HCV ones. © 2006 ieee.

关键词： Image analysis

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