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Robust crease detection in fingerprint images

Robust crease detection in fingerprint images

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Chenyu Wu Jie Zhou Zhao-qi Bian Gang Rong Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0769519008

We study a pattern in the fingerprint called a crease, a kind of stripe which irregularly crosses the normal fingerprint patterns (ridges and valleys). Creases will cause spurious minutiae when using a conventional feature detection algorithm, and therefore decreases the recognition rate of fingerprint identification. By representing the crease using a parameterized rectangle, we design an optimal filter as a detector. We employ a multi-channel filtering framework to detect creases in different orientations. In each channel, PCA is used to extract a rectangle's parameters from the raw detected results. Our algorithm is demonstrated by experiments.

关键词： Robustness Fingerprint recognition image matching Filters Detectors computer vision Filtering Principal component analysis Skin Aging

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Seeing beyond occlusions (and other marvels of a finite lens aperture)

Seeing beyond occlusions (and other marvels of a finite lens...

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conference on computer Vision and pattern recognition (CVPR)

作者： P. Favaro S. Soatto Department of Electrical Engineering Washington University Saint Louis MO USA Department of Computer Science UCLA Los Angeles CA USA

We present a novel algorithm to reconstruct the geometry and photometry of a scene with occlusions from a collection of defocused images. The presence of a finite lens aperture allows us to recover portions of the scene that would be occluded in a pin-hole projection, thus "uncovering" the occlusion. We estimate the shape of each object (a surface, including the occluding boundaries), and its radiance (a positive function defined on the surface, including portions that are occluded by other objects).

关键词： Lenses Apertures Layout computer vision Solid modeling Computational geometry Cameras image generation Shape measurement Humans

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Feature selection for reliable tracking using template matching

Feature selection for reliable tracking using template match...

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conference on computer Vision and pattern recognition (CVPR)

作者： T. Kaneko O. Hori Multimedia Laboratory Corporate R&D Center Toshiba Corporation Kawasaki Japan

A new feature selection method for reliable tracking is presented. In this paper, it is assumed that features are tracked by template matching where small regions around the features are defined as templates. The proposed method selects features based on the upper bound of the average template matching error. This selection criterion is directly related to the reliability of tracking and hence, the performance is better than that of other feature detectors. Experimental results are presented to confirm the efficiency of the proposed method.

关键词： computer vision Upper bound Detectors Laboratories Research and development computer errors image sequences Application software Humans Autocorrelation

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A new graph-theoretic approach to clustering and segmentation

A new graph-theoretic approach to clustering and segmentatio...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Pavan M. Pelillo Dipartimento di Informatica Universitá Ca Foscari di Venezia Mestre Italy

We develop a framework for the image segmentation problem based on a new graph-theoretic formulation of clustering. The approach is motivated by the analogies between the intuitive concept of a cluster and that of a dominant set of vertices, a notion that generalizes that of a maximal complete subgraph to edge-weighted graphs. We also establish a correspondence between dominant sets and the extrema of a quadratic form over the standard simplex, thereby allowing us the use of continuous optimization techniques such as replicator dynamics from evolutionary game theory. Such systems are attractive as they can be coded in a few lines of any high-level programming language, can easily be implemented in a parallel network of locally interacting units, and offer the advantage of biological plausibility. We present experimental results on real-world images which show the effectiveness of the proposed approach.

关键词： image segmentation Clustering algorithms Pixel Game theory computer vision pattern recognition Tree graphs computer languages Biological information theory Proposals

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Object class recognition by unsupervised scale-invariant learning

Object class recognition by unsupervised scale-invariant lea...

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conference on computer Vision and pattern recognition (CVPR)

作者： R. Fergus P. Perona A. Zisserman Department of Engineering Science University of Oxford Oxford UK Department of Electrical Engineering California Institute of Technology Pasadena CA USA

We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for all aspects of the object: shape, appearance, occlusion and relative scale. An entropy-based feature detector is used to select regions and their scale within the image. In learning the parameters of the scale-invariant object model are estimated. This is done using expectation-maximization in a maximum-likelihood setting. In recognition, this model is used in a Bayesian manner to classify images. The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).

关键词： Layout Shape computer vision Detectors Maximum likelihood detection Maximum likelihood estimation image recognition Bayesian methods Solid modeling Animals

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Adaptive pattern discovery for interactive multimedia retrieval

Adaptive pattern discovery for interactive multimedia retrie...

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conference on computer Vision and pattern recognition (CVPR)

作者： Yimin Wu Aidong Zhang Department of Computer Science and Engineering SUNY USA

ISBN: (纸本)0769519008

Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality by extracting a feature subspace using balanced information gain. In the feature subspace, we train an online pattern classification method called adaptive random forests to classify multimedia objects as relevant or irrelevant. Our adaptive random forests adapts the traditional classification method known as random forests for relevance feedback. It improves the efficiency of pattern discovery by choosing the most-informative samples for online learning. Extensive experiments are carried out on a Corel image set (with 31,438 images) to evaluate the performance of our method as compared against the state-of-the-art approaches.

关键词： Feedback Feature extraction image retrieval Machine learning pattern recognition Support vector machines Classification tree analysis Information retrieval Support vector machine classification Decision trees

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Kullback-Leibler boosting

Kullback-Leibler boosting

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Ce Liu Hueng-Yeung Shum Microsoft Research Asia China

ISBN: (纸本)0769519008

In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based on the sum of histogram divergences along corresponding global and discriminating linear features. Second, these linear features, called KL features, are iteratively learnt by maximizing the projected Kullback-Leibler divergence in a boosting manner. Third, the coefficients to combine the histogram divergences are learnt by minimizing the recognition error once a new feature is added to the classifier. This contrasts conventional AdaBoost where the coefficients are empirically set. Because of these properties, KLBoosting classifier generalizes very well. Moreover, to apply KLBoosting to high-dimensional image space, we propose a data-driven Kullback-Leibler Analysis (KLA) approach to find KL features for image objects (e.g., face patches). Promising experimental results on face detection demonstrate the effectiveness of KLBoosting.

关键词： Boosting Face detection Histograms Robustness Support vector machines Support vector machine classification Data analysis image analysis Neural networks Detectors

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Recognizing objects in adversarial clutter: breaking a visual CAPTCHA

Recognizing objects in adversarial clutter: breaking a visua...

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conference on computer Vision and pattern recognition (CVPR)

作者： G. Mori J. Malik Computer Science Division University of California Berkeley Berkeley CA USA

In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZ-Gimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. EZ-Gimpy, currently used by Yahoo, and Gimpy are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.

关键词： Humans Artificial intelligence Object recognition Automatic testing computer science Shape Layout Internet Postal services Dictionaries

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Video-based face recognition using adaptive hidden Markov models

Video-based face recognition using adaptive hidden Markov mo...

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conference on computer Vision and pattern recognition (CVPR)

作者： Xiaoming Liu Tsuhan Cheng Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA U.S.A. Carnegie Mellon University Pittsburgh PA US Electr. & Comput. Eng. Carnegie Mellon Univ. Pittsburgh PA USA

ISBN: (纸本)0769519008

While traditional face recognition is typically based on still images, face recognition from video sequences has become popular. In this paper, we propose to use adaptive hidden Markov models (HMM) to perform video-based face recognition. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. The likelihood scores provided by the HMMs are compared, and the highest score provides the identity of the test video sequence. Furthermore, with unsupervised learning, each HMM is adapted with the test video sequence, which results in better modeling over time. Based on extensive experiments with various databases, we show that the proposed algorithm results in better performance than using majority voting of image-based recognition results.

关键词： Face recognition Hidden Markov models Video sequences Testing Statistics Character recognition Unsupervised learning image databases Voting image recognition

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Motion segmentation with accurate boundaries - a tensor voting approach

Motion segmentation with accurate boundaries - a tensor voti...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Nicolescu G. Medioni Integrated Media Systems Center University of Southern California Los Angeles CA USA

Producing an accurate motion flow field is very difficult at motion boundaries. We present a noniterative approach for segmentation from image motion, based on two voting processes, in different dimensional spaces. By expressing the motion layers as surfaces in a 4D (four-dimensional) space, a voting process is first used to enforce the smoothness of motion and determine an estimation of pixel velocities, motion regions and boundaries. The boundary estimation is then combined with intensity information from the original images in order to locally define a boundary tensor field. The correct boundary is inferred by a 2D (two-dimensional) voting process within this field that enforces the smoothness of boundaries. Finally, correct velocities are computed for the pixels near boundaries, as they are reassigned to different regions. We demonstrate our contribution by analyzing several image sequences, containing multiple types of motion.

关键词： Motion segmentation computer vision Tensile stress Voting image segmentation Motion estimation image analysis image motion analysis image sequence analysis image sequences

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