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Multi-scale phase-based local features

Multi-scale phase-based local features

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Carneiro, Gustavo Jepson, Allan D. Department of Computer Science University of Toronto Toronto Ont. Canada

Local feature methods suitable for image feature based object recognition and for the estimation of motion and structure are composed of two steps, namely the 'where' and 'what' steps. The 'where' step (e.g., interest point detector) must select image points that are robustly localizable under common image deformations and whose neighborhoods are relatively informative. The 'what' step (e.g., local feature extractor) then provides a representation of the image neighborhood that is semi-invariant to image deformations, but distinctive enough to provide model identification. We present a quantitative evaluation of both the 'where' and the 'what' steps for three recent local feature methods: a) phase-based local features [2], b) differential invariants [14], and c) the scale invariant feature transform (SIFT) [9]. Moreover, in order to make the phase-based approach more comparable to the other two approaches, we also introduce a new form of multi-scale interest point detector to be used for its 'where' step. The results show that the phase-based local features lead to better performance than the other two approaches when dealing with common illumination changes, 2D rotation, and sub-pixel translation. On the other hand, the phase-based local features are somewhat more sensitive to scale and large shear changes than the other two methods. Finally, we demonstrate the viability of the phase-based local feature in a simple object recognition system.

关键词： Object recognition

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Joint manifold distance: A new approach to appearance based clustering

Joint manifold distance: A new approach to appearance based ...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Fitzgibbon, Andrew W. Zisserman, Andrew Robotics Research Group Department of Engineering Science University of Oxford Oxford United Kingdom

We wish to match sets of images to sets of images where both sets are undergoing various distortions such as viewpoint and lighting changes. To this end we have developed a Joint Manifold Distance (JMD) which measures the distance between two subspaces, where each subspace is invariant to a desired group of transformations, for example affine warping of the image plane. The JMD may be seen as generalizing invariant distance metrics such as tangent distance in two important ways. First, formally representing priors on the image distribution avoids certain difficulties which, in previous work, have required ad-hoc correction. The second contribution is the observation that previous distances have been computed using what amounted to "home-grown" nonlinear optimizers, and that more reliable results can be obtained by using generic optimizers which have been developed in the numerical analysis community, and which automatically set the parameters which home-grown methods must set by art. The JMD is used in this work to cluster faces in video. Sets of faces detected in contiguous frames define the subspaces, and distance between the subspaces is computed using JMD. In this way the principal cast of a movie can be 'discovered' as the principal clusters. We demonstrate the method on a feature-length movie.

关键词： Object recognition

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Illumination chromaticity estimation using inverse-intensity chromaticity space

Illumination chromaticity estimation using inverse-intensity...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Tan, Robby T. Nishino, Ko Ikeuchi, Katsushi Department of Computer Science The University of Tokyo Tokyo Japan Department of Computer Science Columbia University New York NY United States

Existing color constancy methods cannot handle both uniform colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this paper, we present a single integrated method to estimate illumination chromaticity from single/multi-colored surfaces. Unlike the existing dichromatic-based methods, the proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is-clearly described in "inverse-intensity chromaticity space", a new two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface. Experimental results on real images show the effectiveness of the method.

关键词： Image segmentation

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Clustering appearances of objects under varying illumination conditions

Clustering appearances of objects under varying illumination...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

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

We introduce two appearance-based methods for clustering a set of images of 3-D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.

关键词： Object recognition

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Hue fields and color curvatures: A perceptual organization approach to color image denoising

Hue fields and color curvatures: A perceptual organization a...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Ben-Shahar, Ohad Zucker, Steven W. Department of Computer Science Yale University New Haven CT 06520-8285 United States Dept. of Comp. Sci. and Elec. Eng. Yale University New Haven CT 06520-8285 United States

The denoising of color images is an increasingly studied problem whose state-of-the-art solutions employ a variety of diffusion schemes. Specifying the correct diffusion is difficult, however, in part because of the subtleties of color interactions. We address this difficulty by proposing a perceptual organization approach to color denoising based on the principle of good continuation. We exploit the periodic chromatic (hue) component of the color in its representation as a frame field. We derive two hue curvatures and use them to construct a local model for the behavior of the color, which in turn specifies consistency constraints between nearby color measurements. These constraints are then used to replace noisy pixels by examining their spatial context. Such a contextual analysis (combined with standard methods to handle the scalar channels, saturation and lightness), results in a robust noise removal process that preserves discontinuities, singularities, and fine chromatic structures, including those that diffusion processes are prone to distort. We demonstrate our approach on a variety of synthetic and natural images.

关键词： Color image processing

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Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture

Shape-from-silhouette of articulated objects and its use for...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Cheung, German K.M. Baker, Simon Kanade, Takeo Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States

Shape-From-Silhouette (SFS), also known as Visual Hull (VH) construction, is a popular 3D reconstruction method which estimates the shape of an object from multiple silhouette images. The original SFS formulation assumes that all of the silhouette images are captured either at the same time or while the object is static. This assumption is violated when the object moves or changes shape. Hence the use of SFS with moving objects has been restricted to treating each time instant sequentially and independently. Recently we have successfully extended the traditional SFS formulation to refine the shape of a rigidly moving object over time. Here we further extend SFS to apply to dynamic articulated object, the process of recovering the shape and motion requires two steps: (1) correctly segmenting (points on the boundary of) the silhouettes to each articulated part of the object, (2) estimating the motion of each individual part using the segmented silhouette. In this paper, we propose an iterative algorithm to solve this simultaneous assignment and alignment problem. Once we have estimated the shape and motion of each part of the object, the articulation points between each pair of rigid parts are obtained by solving a simple motion constraint between the connected parts. To validate our algorithm, we first apply it to segment the different body parts and estimate the joint positions of a person. The acquired kinematic (shape and joint) information is then used to track the motion of the person in new video sequences.

关键词： pattern recognition

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Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data

Learning Bayesian network classifiers for facial expression ...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Cohen, Ira Sebe, Nicu Cozman, Fabio G. Cirelo, Marcelo C. Huang, Thomas S. Beckman Institute Univ. of Illinois Urbana-Champaign Urbana IL United States Leiden Inst. of Adv. Computer Sci. Leiden University Leiden Netherlands Escola Politénica Universidade de São Paulo São Paulo Brazil

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that with moderate size labeled training sets and large amount of unlabeled data, our method can utilize unlabeled data to improve classification performance. We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set.

关键词： Face recognition

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Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms

Multi-modal image registration by minimizing Kullback-Leible...

引用

2003 ieee computer society conference on computer vision and pattern recognition, cvpr 2003

作者： Chan, Ho-Ming Chung, Albert C.S. Yu, Simon C.H. Norbash, Alexander Wells III, William M. Department of Computer Science Hong Kong Univ. of Sci. and Technol. Kowloon Hong Kong Dept. Diagn. Radiol. Organ Imaging Prince of Wales Hospital Kowloon Hong Kong Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA United States Artificial Intelligent Laboratory Massachusetts Institute of Technol. Cambridge MA United States

In this paper, we present a new multimodal image registration method based on the a priori knowledge of the class label mappings between two segmented input images. A joint class histogram between the image pairs is estimated by assigning each bin value equal to the total number of occurrences of the corresponding class label pairs. The discrepancy between the observed and expected joint class histograms should be minimized when the transformation is optimal. Kullback-Leibler distance (KLD) is used to measure the difference between these two histograms. Based on the probing experimental results on a synthetic dataset as well as a pair of precisely registered 3D clinical volumes, we showed that, with the knowledge of the expected joint class histogram, our method obtained longer capture range and fewer local optimal points as compared with the conventional Mutual Information (MI) based registration method. We also applied the proposed method to 2D-3D rigid registration problems between DSA and MRA volumes. Based on manually selected markers, we found that the accuracies of our method and the MI-based method are comparable. Moreover, our method is more computationally efficient than the MI-based method.

关键词： Image analysis

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Learning a discriminative classifier using shape context distances

Learning a discriminative classifier using shape context dis...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hao Zhang J. Malik Computer Science Division University of California Berkeley Berkeley CA USA

For the purpose of object recognition, we learn one discriminative classifier based on one prototype, using shape context distances as the feature vector. From multiple prototypes, the outputs of the classifiers are combined using the method called "error correcting output codes". The overall classifier is tested on a benchmark dataset and is shown to outperform existing methods with far fewer prototypes.

关键词： Erbium Object recognition Prototypes Shape measurement Testing Machine vision Nearest neighbor searches Boosting pattern recognition computer science

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Enhancing DPF for near-replica image recognition

Enhancing DPF for near-replica image recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Yan Meng E. Chang Beitao Li Electrical & Computer Engineering Department University of California Santa Barbara USA

Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown to be very effective in near-replica image recognition. DPF, however, suffers from the one-size-fits-all problem: it requires that all pairwise similarity measurements must use the same number of features. We propose methods for enhancing DPF's performance by allowing different numbers of features to be selected in a pairwise manner. Through extensive empirical studies, we show that our three schemes: thresholding, sampling and weighting, and hybrid schemes of these three basic approaches, substantially outperform DPF in near-replica image recognition.

关键词： Image recognition computer society computer vision pattern recognition

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