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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Motion segmentation by em clustering of good features

Motion segmentation by em clustering of good features

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Wong, King Yuen Spetsakis, Minas E. Department of Computer Science Centre for Vision Research York University 4700 Keele Street TorontoONM3J 1P3 Canada

We present a new algorithm that does motion segmentation by tracking small textured patches and then clustering them using EM. A small patch has the advantage that its motion is well modeled by uniform flow and runs a lower risk of boundary inclusion. Inherently, a small patch has less data so it is more susceptible to noise and it is not well suited to fit locally higher order flow models. To overcome these difficulties, we introduce a motion coherence detector to select only the best features and an efficient statistical technique to compute segment-wise affine flow from the EM clustering parameters. We incorporate a residual noise model without any statistical independence assumption and an efficient χ2 test for the noise model to obtain dense segmentation. Computational efficiency is striven for within a rigorous mathematical framework. Experiments with real image sequences show good segments under a variety of conditions. © 2004 ieee.

关键词： Computational efficiency

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Point matching as a classification problem for fast and robust object pose estimation

Point matching as a classification problem for fast and robu...

引用

Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, cvpr 2004

作者： Lepetit, Vincent Pilet, Julien Fua, Pascal Computer Vision Laboratory Swiss Fed. Institute of Technology 1015 Lausanne Switzerland

We propose a novel approach to point matching under large viewpoint and illumination changes that is suitable for accurate object pose estimation at a much lower computational cost than state-of-the-art methods. Most of these methods rely either on using ad hoc local descriptors or on estimating local affine deformations. By contrast, we treat wide baseline matching of keypoints as a classification problem, in which each class corresponds to the set of all possible views of such a point. Given one or more images of a target object, we train the system by synthesizing a large number of views of individual keypoints and by using statistical classification tools to produce a compact description of this view set At run-time, we rely on this description to decide to which class, if any, an observed feature belongs. This formulation allows us to use a classification method to reduce matching error rates, and to move some of the computational burden from matching to training, which can be performed beforehand. In the context of pose estimation, we present experimental results for both planar and non-planar objects in the presence of occlusions, illumination changes, and cluttered backgrounds. We will show that our method is both reliable and suitable for initializing real-time applications.

关键词： Lighting

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Effect of colorspace transformation, the illuminance component, and color modeling on skin detection

Effect of colorspace transformation, the illuminance compone...

引用

Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, cvpr 2004

作者： Jayaram, Sriram Schmugge, Stephen Shin, Min C. Tsap, Leonid V. Department of Computer Science Univ. of North Carolina at Charlotte Charlotte NC 28223 Electronics Engineering Department Lawrence Livermore Natl. Laboratory Livermore CA 94551

Skin detection is an important preliminary process in human motion analysis. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach.

关键词： Object recognition

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Hybrid textons: Modeling surfaces with reflectance and geometry

Hybrid textons: Modeling surfaces with reflectance and geome...

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Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, cvpr 2004

作者： Wang, Jing Dana, Kristin J. Electrical Engineering Department Rutgers University Piscataway NJ United States

The appearance of surface texture as it varies with angular changes of view and illumination is becoming an increasingly important research topic. The bidirectional texture function (BTF) is used in surface modeling because it describes observed image texture as a function of imaging parameters. The BTF has no geometric information, as it is based solely on observed texture appearance. Computational tasks such as recognizing or rendering typically require projecting a sampled BTF to a lower dimensional subspace or clustering to extract representative testons. However, mere is a serious drawback to this approach. Specifically, cast shadowing and occlusions are not fully captured. When recovering the full BTF from a sampled BTF with interpolation, the following two characteristics are difficult or impossible to reproduce: (1) the position and contrast of the shadow border, (2) the movement of the shadow border when the imaging parameters are changed continuously. For a textured surface, the nonlinear effects of cast shadows and occlusions are not negligible. On the contrary, these effects occur throughout the surface and are important perceptual cues to infer surface type. In this paper we present a texture representation that integrates appearance-based information from the sampled BTF with concise geometric information inferred from the sampled BTF. The model is a hybrid of geometric and image-based models and has key advantages in a wide range of tasks, including texture prediction, recognition, and synthesis.

关键词： computer science

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A mathematical comparison of point detectors

A mathematical comparison of point detectors

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Zuliani, M. Kenney, C. Manjunath, B.S. Vision Research Lab Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States

Selecting salient points from two or more images for computing correspondences is a fundamental problem in image analysis. Three methods originally proposed by Harris et al. in [A combined corner and edge detector], by Noble et al. in [Descriptions of image surfaces] and by Shi et al. in [Good features to track] proved to be quite effective and robust and have been widely used by the computer vision community. The goal of this paper is to analyze these point detectors starting from the algebraic and numerical properties of the image auto-correlation matrix. To accomplish this task we will first introduce a "natural" constraint that needs to be satisfied by any point detector based on the auto-correlation matrix. Then, by casting the point detection problem in a mathematical framework based on condition theory [A condition number for point matching with application to registration and post-registration error estimation], we will show that under certain hypothesis the point detectors are equivalent modulo the choice of a specific matrix norm. The results presented in this paper will provide a novel unifying description for the most commonly used point detection algorithms. © 2004 ieee.

关键词： Image analysis

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A real time face tracking and animation system

A real time face tracking and animation system

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Wei, Xiaozhou Zhu, Zhiwei Yin, Lijun Ji, Qiang Department of Computer Science SUNY at Binghamton BinghamtonNY13902 United States Department of ECSE Rensselaer Polytechnic Institute TroyNY12180 United States

In this paper, a novel system for real time face tracking and animation is presented. The system is composed of two major components: (1) real time infra-red (IR) based active facial feature tracking, and (2) real time facial expression generation based on a 3D face avatar. Twenty-two feature points, head pose orientation and eye close-open status are effectively extracted through a video input. Based on the detected facial features, a 3D face model is animated by a dynamic inference algorithm and a transformation from facial motion parameters to facial animation parameters. The work can be extended to the fields of real time facial expression analysis and synthesis for applications of human-computer interaction, model-based video conferencing and low bit rate avatar communication. The performance of the developed system is evaluated by its real time implementation for facial expression generation. © 2004 ieee.

关键词： Human computer interaction

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Cross-modality automatic face model training from large video databases

Cross-modality automatic face model training from large vide...

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Song, Xiaodan Lin, Ching-Yung Sun, Ming-Ting Department of Electrical Engineering University of Washington Box 352500 SeattleWA98195 United States IBM T.J. Watson Research Center 19 Skyline Drive HawthorneNY10532 United States

Face recognition is an important issue on video indexing and retrieval applications. Usually, supervised learning is used to build face models for various specific named individuals. However, a huge amount of labeling work is needed in a traditional supervised learning framework. In this paper, we propose an automatic cross-modality training scheme without supervision which uses automatic speech recognition of videos to build visual face models. Based on Multiple-Instance Learning algorithms, we introduce novel concepts of "Quasi-Positive bags" and "Extended Diverse Density", and use them to develop an automatic training scheme. We also propose to use the "Relative Sparsity" of a cluster to detect the anchorperson in the news videos. Experiments show that our algorithm can get correct models for the persons we are interested in. The automatic learned models are tested and compared with a supervised learning algorithm for face recognition in large news video databases, and show promising results. © 2004 ieee.

关键词： Face recognition

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Generating mosaics with minimum distortions

Generating mosaics with minimum distortions

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Feldman, Doron Zomet, Assaf School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem91904 Israel Computer Science Department Columbia University 500 West 120th Street New YorkNY10027 United States

Manifold mosaicing is a fast and robust way to summarize video sequences captured by a moving camera. It is also useful for rendering compelling 3D visualizations from a video without estimating the 3D structure of the scene. However, since the result mosaics are not perspective images, their geometry is inherently distorted. These mosaics are commonly referred to as multi-perspective images, or multi-perspective mosaics. In this paper we address the following question: Given a video captured by a moving camera, what is the best multi-perspective mosaic that can be generated from it? What is the mosaic with the best combination of large field-of-view and minimal geometric distortions? We define the necessary conditions for a good mosaic and a quantitive criterion for the geometric distortions, and derive analytically the optimal mosaic under this criterion. Results on video sequences confirm that indeed the optimal mosaic has significantly better quality than those generated by other techniques. © 2004 ieee.

关键词： Geometry

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Object class recognition with many local features

Object class recognition with many local features

引用

2004 ieee computer society conference on computer vision and pattern recognition Workshops, cvprW 2004

作者： Helmer, Scott Lowe, David G. Department of Computer Science University of British Columbia VancouverBCV6T 1Z4 Canada

In this paper we present a method to recognize an object class by learning a statistical model of the class. The probabilistic model decomposes the appearance of an object class into a set of local parts and models the appearance, relative location, co-occurrence, and scale of these parts. However, in many object classification approaches that use local features, learning the parameters is exponential in the number of parts because of the problem of matching local features in the image to parts in the model. In this paper we present a learning method that overcomes this difficulty by adding new parts to the model incrementally, using the Maximum-Likelihood framework. When we add a part to the model, a set of candidate parts are selected and the part that increases the likelihood of the data the most is added to the model. Once this part is added to the model, the parameters for all parts up to this point are updated using EM. The learning and recognition in this approach are translation and scale invariant, robust to background clutter, and has less restriction on the number of parts in the model. The validity of the approach is demonstrated on a real world dataset, where the approach is competitive with others, and where the learning for a rich model is much faster than previous approaches. © 2004 ieee.

关键词： Maximum likelihood

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Visual tracking using learned linear subspaces

Visual tracking using learned linear subspaces

引用

Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, cvpr 2004

作者： Ho, Jeffrey Lee, Kuang-Chih Yang, Ming-Hsuan Kriegman, David Computer Science and Engineering University of California San Diego San Diego CA 92093 Computer Science Univ. of Illinois Urbana-Champaign Urbana IL 61801 Honda Research Institute 800 California Street Mountain View CA 94041

This paper presents a simple but robust visual tracking algorithm based on representing the appearances of objects using affine warps of learned linear subspaces of the image space. The tracker adaptively updates this subspace while tracking by finding a linear subspace that best approximates the observations made in the previous frames. Instead of the traditional L2- reconstruction error norm which leads to subsapce estimation using PCA or SVD, we argue that a variant of it, the uniform L2-reconstruction error norm, is the right one for tracking. Under this framework, we provide a simple and a computationally inexpensive algorithm for finding a subspace whose uniform L2-reconstruction error norm for a given collection of data samples is below some threshold, and a simple tracking algorithm is an immediate consequence. We show experimental results on a variety of image sequences of people and man-made objects moving under challenging imaging conditions, which include drastic illumination variation, partial occlusion and extreme pose variation.

关键词： Image processing

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