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IEEE Conference on computer vision and pattern recognition

作者： Xin, Lun Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

ISBN: (纸本)9781424411795

Events can be considered as obvious changes of important properties with semantic meanings. Usually, all these properties are measurable and continual in complex formats and higher dimensions. It is hard to define and measure semantic events on the original observed data. However, according to the perception process of human being, these spatial-temporal continuous data can be mapped onto corresponding smooth manifolds, and different appearances on manifolds can indicate different semantic meanings. In this paper, we propose a semi-supervised learning method, which is based on partially labeled data, to map original observed data onto semantic manifolds for events definition and analysis in dynamic scenes. Furthermore we also perform semantic representations for various events in real world scenes. Finally, we present experimental results to evaluate the performance of our method.

关键词： Supervised learning

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Effective Gaussian mixture learning and shadow suppression for video foreground segmentation

Effective Gaussian mixture learning and shadow suppression f...

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5th International Symposium on Multispectral Image Processing and pattern recognition

作者： Wang, Yong Tian, Jinwen Tan, Yihua Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multi Spectral Informat Proc Techno Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469502

Robust and efficient foreground segmentation is a crucial topic in many computer vision applications. In this paper, we propose an improved method of foreground segmentation with the Gaussian mixture model (GMM) for video surveillance. The number of mixture components of GMM is estimated according to the frequency of pixel value changes, the performance of GMM can be effectively enhanced with the modified background learning and update, new Gaussian distribution generation rule and shadow detection. In order to improve the efficiency, illumination assessment is used to decide whether there are shadows in the given image. Shadow suppression will be adopted based on morphological reconstruction. Besides, the detection of sudden illumination change and background updating are also presented. Results obtained with different real-world scenarios show the robustness and efficiency of the approach.

关键词： video segmentation background subtract mixture Gaussians model shadow removal

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Robust 3D face recognition using learned visual codebook

Robust 3D face recognition using learned visual codebook

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IEEE Conference on computer vision and pattern recognition

作者： Zhong, Cheng Sun, Zhenan Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

ISBN: (纸本)9781424411795

In this paper, we propose a novel learned visual code-book (LVC) for 3D face recognition. In our method, we first extract intrinsic discriminative information embedded in 3D faces using Gabor filters, then K-means clustering is adopted to learn the centers from the filter response vectors. We construct LVC by these learned centers. Finally we represent 3D faces based on LVC and achieve recognition using a nearest neighbor (NN) classifier The novelty of this paper comes from 1) We first apply textons based methods into 3D face recognition;2) We encompass the efficiency of Gabor features for face recognition and the robustness of texton strategy for texture classification simultaneously. Our experiments are based on two challenging databases, CASIA 3D face database and FRGC2.0 3D face database. Experimental results show LVC performs better than many commonly used methods.

关键词： Face recognition

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Approximate nearest subspace search with applications to pattern recognition

Approximate nearest subspace search with applications to pat...

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IEEE Conference on computer vision and pattern recognition

作者： Basri, Ronen Hassner, Tal Zelnik-Manor, Lihi Weizmann Inst Sci IL-76100 Rehovot Israel CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9781424411795

Linear and affine subspaces are commonly used to describe appearance of objects under different lighting, viewpoint, articulation, and identity. A natural problem arising from their use is - given a query image portion represented as a point in some high dimensional space -find a subspace near to the query. This paper presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ tree based search or locality sensitive hashing to find a near subspace. Further speedup may be achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting etc.

关键词： pattern recognition

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Single view human action recognition using key pose matching and viterbi path searching

Single view human action recognition using key pose matching...

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IEEE Conference on computer vision and pattern recognition

作者： Lv, Fengjun Nevatia, Ramakant Univ South Calif Inst Robot & Intelligent Syst Los Angeles CA 90089 USA

ISBN: (纸本)9781424411795

3D human pose recovery is considered as a fundamental step in view-invariant human action recognition. However, inferring 3D poses from a single view usually is slow due to the large number of parameters that need to be estimated and recovered poses are often ambiguous due to the perspective projection. We present an approach that does not explicitly infer 3D pose at each frame. Instead, from existing action models we search for a series of actions that best match the input sequence. In our approach, each action is modeled as a series of synthetic 2D human poses rendered from a wide range of viewpoints. The constraints on transition of the synthetic poses is represented by a graph model called Action Net. Given the input, silhouette matching between the input frames and the key poses is performed first using an enhanced Pyramid Match Kernel algorithm. The best matched sequence of actions is then tracked using the Viterbi algorithm. We demonstrate this approach on a challenging video sets consisting of 15 complex action classes.

关键词： pattern recognition

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Soft edge smoothness prior for alpha channel super resolution

Soft edge smoothness prior for alpha channel super resolutio...

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IEEE Conference on computer vision and pattern recognition

作者： Dai, Shengyang Han, Mei Xu, Wei Wu, Ying Gong, Yihong Northwestern Univ ECCS Dept Evanston IL 60208 USA NEC Labs Amer Inc Cupertino CA 95014 USA

ISBN: (纸本)9781424411795

Effective image prior is necessary for image super resolution, due to its severely under-determined nature. Although the edge smoothness prior can be effective, it is generally difficult to have analytical forms to evaluate the edge smoothness, especially for soft edges that exhibit gradual intensity transitions. This paper finds the connection between the soft edge smoothness and a soft cut metric on an image grid by generalizing the Geocuts method [5], and proves that the soft edge smoothness measure approximates the average length of all level lines in an intensity image. This new finding not only leads to an analytical characterization of the soft edge smoothness prior, but also gives an intuitive geometric explanation. Regularizing the super resolution problem by this new form of prior can simultaneously minimize the length of all level lines, and thus resulting in visually appealing results. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image super resolution, by normalizing edge segments with their alpha channel description, to achieve a unified treatment of edges with different contrast and scale.

关键词： pattern recognition

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Sparse kernels for Bayes optimal discriminant analysis

Sparse kernels for Bayes optimal discriminant analysis

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IEEE Conference on computer vision and pattern recognition

作者： Hamsici, Onur C. Martinez, Aleix M. Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9781424411795

Discriminant Analysis (DA) methods have demonstrated their utility in countless applications in computer vision and other areas of research - especially in the C class classification problem. The most popular approach is Linear DA (LDA), which provides the C-1-dimensional Bayes optimal solution, but only when all the class covariance matrices are identical. This is rarely the case in practice. To alleviate this restriction, Kernel LDA (KLDA) has been proposed. In this approach, we first (intrinsically) map the original nonlinear problem to a linear one and then use LDA to find the C-1-dimensional Bayes optimal subspace. However, the use of KLDA is hampered by its computational cost, given by the number of training samples available and by the limitedness of LDA in providing a C-1-dimensional solution space. In this paper, we first extend the definition of LDA to provide subspace of q < C-1 dimensions where the Bayes error is minimized. Then, to reduce the computational burden of the derived solution, we define a sparse kernel representation, which is able to automatically select the most appropriate sample feature vectors that represent the kernel. We demonstrate the superiority of the proposed approach on several standard datasets. Comparisons are drawn with a large number of known DA algorithms.

关键词： computer vision

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Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND vision 2007年第11期24卷 3403-3417页

作者： Ramos-Michel, Erika M. Kober, Vitaly Ctr Invest Cient & Educ Super Ensenada Dept Comp Sci Ensenada 22860 Baja California Mexico

Generalized correlation filters are proposed to improve recognition of a linearly distorted object embedded in a nonoverlapping background when the input scene is degraded with a linear system and additive noise. Several performance criteria defined for the nonoverlapping signal model are used for the design of filters. The derived filters take into account information about an object to be recognized, disjoint background, noise, and linear degradations of the target and the input scene. computer simulation results obtained with the proposed filters are discussed and compared with those of various correlation filters in terms of discrimination capability, location errors, and tolerance to input noise. (c) 2007 Optical Society of America.

关键词： computer simulation Image processing Matched filtering pattern recognition Phase only filters Synthetic discrimination functions

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Visual curvature

Visual curvature

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IEEE Conference on computer vision and pattern recognition

作者： Liu, HaiRong Latecki, Longin Jan Liu, WenYu Bai, Xiang HuaZhong Univ Sci & Technol Wuhan Peoples R China Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781424411795

In this paper, we propose a new definition of curvature, called visual curvature. It is based on statistics of the extreme points of the height functions computed over all directions. By gradually ignoring relatively small heights, a single parameter multi-scale curvature is obtained. It does not mode the original contour and the scale parameter has an obvious geometric meaning. The theoretical properties and the experiments presented demonstrate that multi-scale visual curvature is stable, even in the presence of significant noise. In particular, it can deal with contours with significant gaps. We also show a relation between multi-scale visual curvature and convexity of simple closed curves. To our best knowledge, the proposed definition of visual curvature is the first ever that applies to regular curves as defined in differential geometry as well as to turn angles of polygonal curves. Moreover, it yields stable curvature estimates of curves in digital images even under sever distortions.

关键词： pattern recognition

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Visual odometry system using multiple stereo cameras and inertial measurement unit

Visual odometry system using multiple stereo cameras and ine...

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IEEE Conference on computer vision and pattern recognition

作者： Oskiper, Taragay Zhu, Zhiwei Samarasekera, Supun Kumar, Rakesh Sarnoff Corp 201 Washington Rd Princeton NJ 08540 USA

ISBN: (纸本)9781424411795

Over the past decade, tremendous amount of research activity has focused around the problem of localization in GPS denied environments. Challenges with localization are highlighted in human wearable systems where the operator can freely move through both indoors and outdoors. In this paper, we present a robust method that addresses these challenges using a human wearable system with two pairs of backward and forward looking stereo cameras together with an inertial measurement unit (IMU). This algorithm can run in real-time with 15Hz update rate on a dual-core 2GHz laptop PC and it is designed to be a highly accurate local (relative) pose estimation mechanism acting as the front-end to a Simultaneous Localization and Mapping (SLAM) type method capable of global corrections through landmark matching. Extensive tests of our prototype system so far, reveal that without any global landmark matching, we achieve between 0.5% and 1% accuracy in localizing a person over a 500 meter travel indoors and outdoors. To our knowledge, such performance results with a real time system have not been reported before.

关键词： computer vision

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