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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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Online appearance model learning for video-based face recognition

Online appearance model learning for video-based face recogn...

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IEEE Conference on computer vision and pattern recognition

作者： Liu, Liang Wang, Yunhong Tan, Tieniu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781424411795

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper First, we propose an algorithm which can learn appearance models online without using a pretrained model. Second, we propose a methodfor eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.

关键词： Face recognition

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Fourier methods for nonparametric image registration

Fourier methods for nonparametric image registration

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IEEE Conference on computer vision and pattern recognition

作者： Cahill, Nathan D. Noble, J. Alison Hawkes, David J. Carestream Hlth Inc Res & Innovat Rochester NY 14608 USA Univ Oxford Wolfson Med Vision Lab Oxford OX1 2JD England UCL Ctr Med Image Comp London WC1E 6BT England

ISBN: (纸本)9781424411795

Nonparametric image registration algorithms use deformation fields to define nonrigid transformations relating two images. Typically, these algorithms operate by successively solving linear systems of partial differential equations. These PDE systems arise by linearizing the Euler-Lagrange equations associated with the minimization of a functional defined to contain an image similarity term and a regularizer. Iterative linear system solvers can be used to solve the linear PDE systems, but they can be extremely slow. Some faster techniques based on Fourier methods, multigrid methods, and additive operator splitting, existfor solving the linear PDE systemsfor specific combinations of regularizers and boundary conditions. In this paper, we show that Fourier methods can be employed to quickly solve the linear PDE systems for every combination of standard regularizers (diffusion, curvature, elastic, and fluid) and boundary conditions (Dirichlet, Neumann, andperiodic).

关键词： Image registration

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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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Using genetic algorithms to improve matching performance of changeable biometrics from combining PCA and ICA methods

Using genetic algorithms to improve matching performance of ...

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IEEE Conference on computer vision and pattern recognition

作者： Jeong, Minyi Choi, Jeung-Yoon Kim, Jaihie Yonsei Univ Sch Elect & Elect Engn BERC Seoul 120749 South Korea

ISBN: (纸本)9781424411795

Biometrics is personal authentication which uses an individual's information. In terms of user authentication, biometric systems have many advantages. However, despite its advantages, they also have some disadvantages in the area of privacy problems. Changeable biometrics is solution to problem of privacy protection. In this paper we propose a changeable face biometrics system to overcome this problem. The proposed method uses the PCA and ICA methods and genetic algorithms. PCA and ICA coefficient vectors extracted from an input face image were normalized using their norm. The two normalized vectors were transformed using a weighting matrix which is derived using genetic algorithms and then scrambled randomly. A new transformed face coefficient vector was generated by addition of the two weighted normalized vectors. Through experiments, we see that we can achieve performance accuracy that is better than conventional methods. And, it is also shown that the changeable templates are non-invertible and provide sufficient reproducibility.

关键词： pattern matching

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A New Robust Image Feature Point Detector 11

A New Robust Image Feature Point Detector

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11th International Symposium on Multispectral Image Processing and pattern recognition (mippr) - Automatic Target recognition and Navigation

作者： Zhao, Yi HuBei Presch Teacher Coll Wuhan 430223 Peoples R China

ISBN: (纸本)9781510636361

A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noise efficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal and initial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, the experiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidian distance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in all regions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the new robust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that of the points without those limitations.

关键词： Feature Point Detection computer vision Noise Smooth Space Limitation Robustness

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Efficient indexing for articulation invariant shape matching and retrieval

Efficient indexing for articulation invariant shape matching...

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IEEE Conference on computer vision and pattern recognition

作者： Biswas, Soma Aggarwal, Gaurav Chellappa, Rama Univ Maryland Ctr Automat Res UMICAS Dept ECE College Pk MD 20742 USA Univ Maryland Ctr Automat Res UMICAS Dept Comp Sci College Pk MD 20742 USA

ISBN: (纸本)9781424411795

Most shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations and rigid transformations. The features characterize pairwise geometric relationships between interest points on the shape, thereby providing robustness to the approach. Shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Even for a moderate size database of 1000 shapes, the retrieval process is several times faster than most techniques with similar performance. Extensive experimental results are presented to illustrate the advantages of our approach as compared to the best in the field.

关键词： pattern matching

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Multi-label image segmentation via max-sum solver

Multi-label image segmentation via max-sum solver

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IEEE Conference on computer vision and pattern recognition

作者： Micusik, Banislav Pajdla, Tomas Vienna Univ Technol Inst Comp Aided Automat Pattern Recognit & Image Proc Grp Vienna Austria Czech Tech Univ Ctr Machine Precept Dpt Cybermet Prague Czech Republic

ISBN: (纸本)9781424411795

We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the Global Consistency Error evaluated on the Berkeley segmentation database.

关键词： Image segmentation

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A sequential Monte Carlo approach to anomaly detection in tracking visual events

A sequential Monte Carlo approach to anomaly detection in tr...

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IEEE Conference on computer vision and pattern recognition

作者： Cui, Peng Sun, Li-Feng Liu, Zhi-Qiang Yang, Shi-Qiang Tsinghua Univ Comp Sci Beijing 100084 Peoples R China City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China

ISBN: (纸本)9781424411795

In this paper we propose a technique to detect anomalies in individual and interactive event sequences. We categorize anomalies into two classes: abnormal event, and abnormal context, and model them in the Sequential Monte Carlo framework which is extended by Markov Random Field for tracking interactive events. Firstly, we propose a novel pixel-wise event representation method to construct feature images, in which each blob corresponds to a visual event. Then we transform the original blob-level features into subspaces to model probabilistic appearance manifolds for each event-class. With the probability of an observation associated with each event-class (or state) derived from probabilistic manifolds, and state transitional probability, the prior and posterior state distributions can be estimated. We demonstrate in experiments that the approach can reliably detect such anomalies with low false alarm rates.

关键词： computer vision

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Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

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MACHINE vision AND APPLICATIONS 2013年第1期24卷 187-204页

作者： Molina, Javier Escudero-Vinolo, Marcos Signoriello, Alessandro Pardas, Montse Ferran, Christian Bescos, Jesus Marques, Ferran Martinez, Jose M. Univ Autonoma Madrid Escuela Politecn Super Video Proc & Understanding Lab Lab C111 E-28049 Madrid Spain Univ Politecn Cataluna Image & Video Proc Grp Barcelona Spain Telefon I D Barcelona Spain

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.

关键词： computer vision Human-computer interaction Hand gesture cognition

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