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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是181-190 订阅

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Gauging Association patterns of Chromosome Territories via Chromatic Median

Gauging Association Patterns of Chromosome Territories via C...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ding, Hu Stojkovic, Branislav Berezney, Ronald Xu, Jinhui SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA SUNY Buffalo Dept Biol Sci Buffalo NY 14260 USA

ISBN: (纸本)9780769549897

Computing accurate and robust organizational patterns of chromosome territories inside the cell nucleus is critical for understanding several fundamental genomic processes, such as co-regulation of gene activation, gene silencing, X chromosome inactivation, and abnormal chromosome rearrangement in cancer cells. the usage of advanced fluorescence labeling and image processing techniques has enabled researchers to investigate interactions of chromosome territories at large spatial resolution. the resulting high volume of generated data demands for high-throughput and automated image analysis methods. In this paper, we introduce a novel algorithmic tool for investigating association patterns of chromosome territories in a population of cells. Our method takes as input a set of graphs, one for each cell, containing information about spatial interaction of chromosome territories, and yields a single graph that contains essential information for the whole population and stands as its structural representative. We formulate this combinatorial problem as a semi-definite programming and present novel techniques to efficiently solve it. We validate our approach on both artificial and real biological data;the experimental results suggest that our approach yields a near-optimal solution, and can handle large-size datasets, which are significant improvements over existing techniques.

关键词： cellular biophysics graph theory medical image processing organisational aspects pattern classification

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Deep Convolutional Network Cascade for Facial Point Detection

Deep Convolutional Network Cascade for Facial Point Detectio...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

We propose a new approach for estimation of the positions of facial keypoints with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy keypoints. there are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each keypoint. Second, since the networks are trained to predict all the keypoints simultaneously, the geometric constraints among keypoints are implicitly encoded. the method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. the networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the- art methods in both detection accuracy and reliability(1).

关键词： face recognition feature extraction hidden feature removal image texture pose estimation

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Anti-spoofing in action: joint operation with a verification system

Anti-spoofing in action: joint operation with a verification...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chingovska, Ivana Anjos, Andre Marcel, Sebastien Idiap Res Inst CH-1920 Martigny Switzerland

ISBN: (纸本)9780769549903

Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision-level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different spoofing counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use-case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks.

关键词： biometrics (access control) face recognition image classification security of data sensor fusion

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Evaluating Open-Universe Face Identification on the Web

Evaluating Open-Universe Face Identification on the Web

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Becker, Brian C. Ortiz, Enrique G. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9780769549903

Face recognition is becoming a widely used technique to organize and tag photos. Whether searching, viewing, or organizing photos on the web or in personal photo albums, there is a growing demand to index real-world photos by the subjects in them. Even consumer platforms such as Google Picasa, Microsoft Photo Gallery, and social network sites such as Facebook have integrated forms of automated face tagging and recognition;furthermore, a number of libraries and cloud-based APIs that perform face recognition have become available. With such a plethora of choices, comparisons of recent advances become more important to gauge the state of progress in the field. this paper evaluates face identification in the context of not only research algorithms, but also considers consumer photo products, client-side libraries, and cloud-based APIs on a new, large-scale dataset derived from PubFig83 and LFW in a realistic open-universe scenario.

关键词： application program interfaces cloud computing face recognition

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A NURBS-based spectral reflectance descriptor with applications in computer vision and pattern recognition

A NURBS-based spectral reflectance descriptor with applicati...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Cong, Phuoc Huynh Robles-Kelly, Antonio RSISE Bldg. 115 Australian National University Canberra ACT 0200 Australia Locked Bag 8001 Canberra ACT 2601 Australia

ISBN: (纸本)9781424422432

In this paper, we present a surface reflectance descriptor based on the control points resulting from the interpolation of Non-Uniform Rational B-Spline (NURBS) curves to multispectral reflectance data. the interpolation is based upon a knot removal scheme in the parameter domain. thus, we exploit the local support of NURBS so as to recover a compact descriptor robust to noise and local perturbation of the spectra. We demonstrate the utility of our NURBS-based descriptor for material identification. To this end, we perform skin spectra recognition making use of a Support Vector Machine classifier. We also provide results on hyperspectral imagery and elaborate on the preprocessing step for skin segmentation. We compare our results with those obtained using an alternative descriptor. ©2008 ieee.

关键词： Interpolation

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Collective Activity Detection using Hinge-loss Markov Random Fields

Collective Activity Detection using Hinge-loss Markov Random...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： London, Ben Khamis, Sameh Bach, Stephen H. Huang, Bert Getoor, Lise Davis, Larry Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9780769549903

We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HL-MRFs via familiar constructs from first-order logic. We apply HL-MRFs to the task of activity detection, using principles of collective classification. Our model is simple, intuitive and interpretable. We evaluate our model on two datasets and show that it achieves significant lift over the low-level detectors.

关键词： Markov processes computer vision image classification object detection probabilistic logic random processes

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Emotional Expression Classification using Time-Series Kernels

Emotional Expression Classification using Time-Series Kernel...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lorincz, Andras Jeni, Laszlo A. Szabo, Zoltan Cohn, Jeffrey F. Kanade, Takeo Eotvos Lorand Univ Budapest Hungary Carnegie Mellon Univ Pittsburgh PA 15106 USA Univ Pittsburgh Pittsburgh PA 15106 USA

ISBN: (纸本)9780769549903

Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures. We achieved over 99% accuracy - measured by area under ROC curve - using only the 'motion pattern' of the PCA compressed representation of the marker point vector, the so-called shape parameters. Beyond the classification of full motion patterns, several expressions were recognized with over 90% accuracy in as few as 5-6 frames from their onset, about 200 milliseconds.

关键词： emotion recognition face recognition image motion analysis principal component analysis support vector machines time series time warp simulation

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Action recognition with Temporal Relationships

Action Recognition with Temporal Relationships

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Guangchun Wan, Yiwen Santiteerakul, Wasana Tang, Shijun Buckles, Bill P. Univ N Texas Denton TX 76203 USA

ISBN: (纸本)9780769549903

Action recognition is an important component in human-machine interactive systems and video analysis. Besides low-level actions, temporal relationships are also important for many actions, which are not fully studied for recognizing actions. We model the temporal structure of low-level actions based on dense trajectory groups. Trajectory groups are a higher level and more meaningful representation of actions than raw individual trajectories. Based on the temporal ordering of trajectory groups, we describe the temporal structure using Allen's temporal relations in a discriminative manner, and combine it with a generative model using bag-of-words. the simple idea behind the model is to extract mid-level features from domain-independent dense trajectories and classify the actions by exploring the temporal structure among them based on a set of Allen's relations. We compare the proposed approach with bag-of-words representation using public datasets, and the results show that our approach improves recognition accuracy.

关键词： feature extraction image recognition image representation

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LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification

LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ptucha, Raymond Savakis, Andreas Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9780769549903

Sparse representations have successfully been exploited for the development of highly accurate classifiers. Unfortunately, these classifiers are computationally intensive and subject to the adverse effects of coefficient contamination, where for example variations in pose may affect identity and expression recognition. We propose a technique, called LGE-KSVD, that addresses both problems and attains state-of-the-art results for face and gesture classification problems. Specifically, LGE-KSVD utilizes variants of Linear extension of Graph Embedding to optimize K-SVD, an iterative technique for small yet overcomplete dictionary learning. the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based linear classifier are jointly learned through LGE-KSVD. the atom optimization process is redefined to have variable support using graph embedding techniques to produce a more flexible and elegant dictionary learning algorithm. Results are obtained for a wide variety of facial and activity recognition problems to demonstrate the robustness of the proposed method.

关键词： dictionaries face recognition graph theory image classification image representation learning (artificial intelligence) object recognition optimisation singular value decomposition

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Robust Multi-Resolution Pedestrian Detection in Traffic Scenes

Robust Multi-Resolution Pedestrian Detection in Traffic Scen...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yan, Junjie Zhang, Xucong Lei, Zhen Liao, Shengcai Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Ctr Biometr & Secur Res Natl Lab Pattern Recognition Beijing Peoples R China

ISBN: (纸本)9780769549897

the serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. the model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. the context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).

关键词： image resolution object detection pedestrians traffic engineering computing

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