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Reconstruction in the Round Using Photometric Normals and Silhouettes.

Reconstruction in the Round Using Photometric Normals and Si...

引用

conference on computer vision and pattern recognition (CVPR)

作者： G. Vogiatzis C. Hernandez R. Cipolla Department of Engineering University of Cambridge Cambridge UK

This paper addresses the problem of obtaining complete, detailed reconstructions of shiny textureless objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under varying illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint and produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object’s visual hull. This is then used to recover the illumination and initialise a multi-view photometric stereo scheme to obtain a closed surface reconstruction. The contributions of the paper are twofold: Firstly we describe a robust technique to estimate light directions and intensities and secondly, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and hence allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with a complete reconstruction of a challenging real object.

关键词： Photometry Image reconstruction Stereo vision Surface reconstruction Stereo image processing Shape Lighting Cameras computer vision Reflectivity

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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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Identifying Color in Motion in Video Sensors

Identifying Color in Motion in Video Sensors

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gang Wu A. Rahimi E.Y. Chang Kingshy Goh T. Tsai Ankur Jain Yuan-Fang Wang Electrical and Computer Engineering UC Santa Barbara CA Proximex Inc Cupertino CA Computer Science UC Santa Barbara CA Intel Inc Santa Clara CA

Identifying or matching the surface color of a moving object in surveillance video is critical for achieving reliable object-tracking and searching. Traditional color models provide little help, since the surface of an object is usually not flat, the object’s motion can alter the surface’s orientation, and the lighting conditions can vary when the object moves. To tackle this research problem, we conduct extensive data mining on video clips collected under various lighting conditions and distances from several video-cameras. We observe how each of the eleven culture colors can drift in the color space when an object’s surface is in motion. In the color space, we then learn the drift pattern of each culture color for classifying unseen surface colors. Finally, we devise a distance function taking color drift into consideration to perform color identification and matching. Empirical studies show our approach to be very promising: achieving over 95% color-prediction accuracy.

关键词： Cameras Lighting Surveillance Colored noise Clothing Light sources Optical reflection computer science Data mining Shape

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Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking

Efficient Nonparametric Belief Propagation with Application ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： T.X. Han Huazhong Ning T.S. Huang Beckman Institute and ECE Department University of Illinois Urbana-Champaign Urbana-Champaign IL USA

An efficient Nonparametric Belief Propagation (NBP) algorithm is developed in this paper. While the recently proposed nonparametric belief propagation algorithm has wide applications such as articulated tracking [22, 19], superresolution [6], stereo vision and sensor calibration [10], the hardcore of the algorithm requires repeatedly sampling from products of mixture of Gaussians, which makes the algorithm computationally very expensive. To avoid the slow sampling process, we applied mixture Gaussian density approximation by mode propagation and kernel fitting [2, 7]. The products of mixture of Gaussians are approximated accurately by just a few mode propagation and kernel fitting steps, while the sampling method (e.g. Gibbs sampler) needs many samples to achieve similar approximation results. The proposed algorithm is then applied to articulated body tracking for several scenarios. The experimental results show the robustness and the efficiency of the proposed algorithm. The proposed efficient NBP algorithm also has potentials in other applications mentioned above.

关键词： Belief propagation Sampling methods Inference algorithms Gaussian approximation Kernel Random variables computer vision Graphical models Robustness Gaussian processes

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Supervised Learning of Edges and Object Boundaries

Supervised Learning of Edges and Object Boundaries

引用

conference on computer vision and pattern recognition (CVPR)

作者： P. Dollar Zhuowen Tu S. Belongie Computer Science & Engineering University of California San Diego USA Lab of Neuro Imaging University of California Los Angeles USA

Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made purely based on low level cues such as gradient, instead we need to engage all levels of information, low, middle, and high, in order to decide where to put edges. In this paper we propose a novel supervised learning algorithm for edge and object boundary detection which we refer to as Boosted Edge Learning or BEL for short. A decision of an edge point is made independently at each location in the image; a very large aperture is used providing significant context for each decision. In the learning stage, the algorithm selects and combines a large number of features across different scales in order to learn a discriminative model using an extended version of the Probabilistic Boosting Tree classification algorithm. The learning based framework is highly adaptive and there are no parameters to tune. We show applications for edge detection in a number of specific image domains as well as on natural images. We test on various datasets including the Berkeley dataset and the results obtained are very good.

关键词： Supervised learning Image edge detection Object detection computer science computer vision Apertures Boosting Application software Detectors Neuroimaging

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Robust Statistical Estimation and Segmentation of Multiple Subspaces

Robust Statistical Estimation and Segmentation of Multiple S...

引用

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

作者： A.Y. Yang S.R. Rao Yi Ma Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana-Champaign IL USA

We study the problem of estimating a mixed geometric model of multiple subspaces in the presence of a significant amount of outliers. The estimation of multiple subspaces is an important problem in computer vision, particularly for segmenting multiple motions in an image sequence. We first provide a comprehensive survey of robust statistical techniques in the literature, and identify three main approaches for detecting and rejecting outliers. Through a careful examination of these approaches, we propose and investigate three principled methods for robustly estimating mixed subspace models: random sample consensus, the influence function, and multivariate trimming. Using a benchmark synthetic experiment and a set of real image sequences, we conduct a thorough comparison of the three methods

关键词： Robustness Motion estimation computer vision Image segmentation Layout Image sequences Solid modeling Geometry Face detection Cameras

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A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms

A Comparison and Evaluation of Multi-View Stereo Reconstruct...

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conference on computer vision and pattern recognition (CVPR)

作者： S.M. Seitz B. Curless J. Diebel D. Scharstein R. Szeliski University of Washington USA University of Stanford USA Middlebury College Microsoft Research Limited UK

This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://***/mview.

关键词： Reconstruction algorithms Stereo image processing Stereo vision Layout Taxonomy Image reconstruction Shape measurement Educational institutions Cameras Image databases

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Perceptually-Inspired and Edge-Directed Color Image Super-Resolution

Perceptually-Inspired and Edge-Directed Color Image Super-Re...

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conference on computer vision and pattern recognition (CVPR)

作者： Yu-Wing Tai Wai-Shun Tong Chi-Keung Tang Vision and Graphics Group Hong Kong University of Science and Technology Hong Kong China

Inspired by multi-scale tensor voting, a computational framework for perceptual grouping and segmentation, we propose an edge-directed technique for color image superresolution given a single low-resolution color image. Our multi-scale technique combines the advantages of edgedirected, reconstruction-based and learning-based methods, and is unique in two ways. First, we consider simultaneously all the three color channels in our multi-scale tensor voting framework to produce a multi-scale edge representation to guide the process of high-resolution color image reconstruction, which is subject to the back projection constraint. Fine details are inferred without noticeable blurry or ringing artifacts. Second, the inference of highresolution curves is achieved by multi-scale tensor voting, using the dense voting field as an edge-preserving smoothness prior which is derived geometrically without any timeconsuming learning procedure. Qualitative and quantitative results indicate that our method produces convincing results in complex test cases typically used by state-of-theart image super-resolution techniques.

关键词： Color Image resolution Image reconstruction Tensile stress Image edge detection Electronic voting computer vision Nearest neighbor searches Level set Graphics

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Image Intensification for Low-Light Face recognition

Image Intensification for Low-Light Face Recognition

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

作者： D.A. Socolinsky L.B. Wolff A.J. Lundberg Equinox Corporation Baltimore MD USA Equinox Corporation New York NY USA

This paper presents the first published systematic study of face recognition performance as a function of light level using intensified near infrared imagery. This technology is the most prevalent in both civilian and military night vision equipment, and provides enough intensification for human operators to perform standard tasks under extremely low-light conditions. We describe a comprehensive data collection effort undertaken by the authors to image subjects under carefully controlled illumination and quantify the performance of standard face recognition algorithms on visible and intensified imagery as a function of light level. Performance comparisons for automatic face recognition are reported using the standardized implementations from the CSU Face Identification Evaluation System. The results contained in this paper should constitute the initial step for analysis and deployment of face recognition systems designed to work in low-light level condi

关键词： Image intensifiers Face recognition Infrared imaging Optical imaging Military standards Night vision Lighting control Image recognition Military equipment Humans

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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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