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IEEE Conference on computer vision and pattern recognition

作者： Tappen, Marshall F. Liu, Ce Adelson, Edward H. Freeman, William T. Univ Cent Florida Orlando FL 32816 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781424411795

Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented using matrix and linear algebra routines. However, recent research has focused on on discrete-valued and non-convex MRF models because Gaussian models tend to over-smooth images and blur edges. In this paper, we show how to train a Gaussian Conditional Random Field (GCRF) model that overcomes this weakness and can outperform the non-convex Field of Experts model on the task of denoising images. A key advantage of the GCRF model is that the parameters of the model can be optimized efficiently on relatively large images. The competitive performance of the GCRF model and the ease of optimizing its parameters make the GCRF model an attractive option for vision and image processing applications.

关键词： Markov processes

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A minutiae-based fingerprint individuality model

A minutiae-based fingerprint individuality model

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IEEE Conference on computer vision and pattern recognition

作者： Chen, Jiansheng Moon, Yiu-Sang Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781424411795

Fingerprint individuality study deals with the crucial problem of the discriminative power of fingerprints for recognizing people. In this paper, we present a novel fingerprint individuality model based on minutiae, the most commonly used fingerprint feature. The probability of the false correspondence among fingerprints from different fingers is calculated by combining the distinctiveness of the spatial locations and directions of the minutiae. To validate our model, experiments were performed using different fingerprint databases. The matching score distribution predicted by our model actually fits the observed experimental results satisfactorily. Comparing to most previous fingerprint individuality models, our model makes more reasonably conservative estimate of the fingerprint discriminative power, making it a powerful tool for studying the fingerprint individuality as well as the performance evaluation of fingerprint verification systems.

关键词： pattern matching

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Quasi-dense wide baseline matching using match propagation

Quasi-dense wide baseline matching using match propagation

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IEEE Conference on computer vision and pattern recognition

作者： Kannala, Juho Brandt, Sarni S. Univ Oulu Machine Vis Grp Oulu Finland

ISBN: (纸本)9781424411795

In this paper we propose extensions to the match propagation algorithm which is a technique for computing quasidense point correspondences between two views. The extensions make the match propagation applicable for wide baseline matching, i.e., for cases where the camera pose can vary a lot between the views. Our first extension is to use a local affine model for the geometric transformation between the images. The estimate of the local transformation is obtained from affine covariant interest regions which are used as seed matches. The second extension is to use the second order intensity moments to adapt the current estimate of the local affine transformation during the propagation. This allows a single seed match to propagate into regions where the local transformation between the views differs from the initial one. The experiments with real data show that the proposed techniques improve both the quality and coverage of the quasi-dense disparity map.

关键词： pattern matching

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Visual learning by evolutionary and coevolutionary feature synthesis

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2007年第5期11卷 635-650页

作者： Krawiec, Krzysztof Bhanu, Bir Poznan Tech Univ Inst Comp Sci PL-60965 Poznan Poland Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. The training coevolves feature extraction procedures, each being a sequence of elementary image processing and computer vision operations applied to input images. Extensive experimental results show that the approach attains competitive performance for three-dimensional object recognition in real synthetic aperture radar imagery.

关键词： computer vision (CV) cooperative coevolution (CC) evolutionary computation (EC) machine learning (ML) pattern recognition visual learning

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Human Gait recognition Based on Kernel PCA Using Projections

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Journal of computer Science & Technology 2007年第6期22卷 867-876页

作者： Murat Ekinci Murat Aykut Computer Vision Lab Department of Computer EngineeringKaradeniz Technical University61080 TrabzonTurkey

This paper presents a novel approach for human identification at a distance using gait recognition. recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

关键词： biometrics gait recognition gait representation kernel PCA pattern recognition

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Multiview normal field integration using level set methods

Multiview normal field integration using level set methods

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IEEE Conference on computer vision and pattern recognition

作者： Chang, Ju Yong Lee, Kyoung Mu Lee, Sang Uk Seoul Natl Univ Sch Elect Eng Seoul 151600 South Korea

ISBN: (纸本)9781424411795

In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shading and photometric stereo that reconstruct a 2.5D surface using a single-view normal field, our algorithm can combine multiview normal fields simultaneously and recover the full 3D shape of a target object. We formulate this multiview normal integration problem by an energy minimization framework and find an optimal solution in a least square sense using a variational technique. A level set method is applied to solve the resultant geometric PDE that minimizes the proposed error functional. It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows. In particular, we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting. Experimental results for various synthetic data show the validity of our approach.

关键词： computer vision

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Resolving objects at higher resolution from a single motion-blurred image

Resolving objects at higher resolution from a single motion-...

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IEEE Conference on computer vision and pattern recognition

作者： Agrawal, Amit Raskar, Ramesh MERL 201 Broadway Cambridge MA 02139 USA

ISBN: (纸本)9781424411795

Motion blur can degrade the quality of images and is considered a nuisance for computer vision problems. In this paper, we show that motion blur can in-fact be used for increasing the resolution of a moving object. Our approach utilizes the information in a single motion-blurred image without any image priors or training images. As the blur size increases, the resolution of the moving object can be enhanced by a larger factor albeit with a corresponding increase in reconstruction noise. Traditionally, motion deblurring and super-resolution have been ill-posed problems. Using a coded-exposure camera that preserves high spatial frequencies in the blurred image, we present a linear algorithm for the combined problem of deblurring and resolution enhancement and analyze the invertibility of the resulting linear system. We also show a method to selectively enhance the resolution of a narrow region of high-frequency features, when the resolution of the entire moving object cannot be increased due to small motion blur Results on real images showing up to four times resolution enhancement are presented.

关键词： Image resolution Spatial resolution Degradation computer vision Image reconstruction Cameras Frequency Image analysis Algorithm design and analysis Linear systems

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Capturing people in surveillance video

Capturing people in surveillance video

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IEEE Conference on computer vision and pattern recognition

作者： Feris, Rogerio Tian, Ying-Li Hampapur, Arun IBM Corp Thomas J Watson Res Ctr POB 704 Yorktown Hts NY 10598 USA

ISBN: (纸本)9781424411795

This paper presents reliable techniques for detecting, tracking, and storing keyframes of people in surveillance video. The first component of our system is a novel face detector algorithm, which is based on first learning local adaptive features for each training image, and then using Adaboost learning to select the most general features for detection. This method provides a powerful mechanism for combining multiplefeatures, allowing Jaster training time and better detection rates. The second component is a face tracking algorithm that interleaves multiple view-based classifiers along the temporal domain in a video sequence. This interleaving technique, combined with a correlation-based tracker, enables fast and robust face tracking over time. Finally, the third component of our system is a keyframe selection method that combines a person classifier with a face classifier The basic idea is to generate a person keyframe in case the face is not visible, in order to reduce the number of false negatives. We performed quantitatively evaluation of our techniques on standard datasets and on surveillance videos captured by a camera over several days.

关键词： computer vision

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Optimizing distribution-based matching by random subsampling

Optimizing distribution-based matching by random subsampling

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IEEE Conference on computer vision and pattern recognition

作者： Leung, Alex Po Gong, Shaogang Queen Mary Univ London Dept Comp Sci Mile End Rd London E1 4NS England

ISBN: (纸本)9781424411795

We boost the efficiency and robustness of distributionbased matching by random subsampling which results in the minimum number of samples required to achieve a specified probability that a candidate sampling distribution is a good approximation to the model distribution. The improvement is demonstrated with applications to object detection, Mean-Shift tracking using color distributions and tracking with improved robustness for low-resolution video sequences. The problem of minimizing the number of samples required for robust distribution matching is formulated as a constrained optimization problem with the specified probability as the objective function. We show that surprisingly Mean-Shift tracking using our method requires very few samples. Our experiments demonstrate that robust tracking can be achieved with even as few as S random samples from the distribution of the target candidate. This leads to a considerably reduced computational complexity that is also independent of object size. We show that random subsampling speeds up tracking by two orders of magnitude for typical object sizes.

关键词： pattern recognition

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EDA approach for model based localization and recognition of vehicles

EDA approach for model based localization and recognition of...

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IEEE Conference on computer vision and pattern recognition

作者： Zhang, Zhaoxiang Dong, Weishan Huang, Kaiqi Tan, Tieniu. Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100864 Peoples R China

ISBN: (纸本)9781424411795

We address the problem of model based recognition. Our aim is to localize and recognize road vehicles from monocular images in calibrated scenes. A deformable 3D geometric vehicle model with 12 parameters is set up as prior information and Bayesian Classification Error is adopted for evaluation of fitness between the model and images. Using a novel evolutionary computing method called EDA (Estimation of Distribution Algorithm), we can not only determine the 3D pose of the vehicle, but also obtain a 12 dimensional vector which corresponds to the 12 shape parameters of the model. By clustering obtained vectors in the parameter space, we can recognize different types of vehicles. Experimental results demonstrate the effectiveness of the approach to vehicles of different types and poses. Thanks to EDA, we can not only localize and recognize vehicles, but also show the whole evolution procedure of the deformable model which gradually fits the image better and better.

关键词： Object recognition

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