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检索条件"任意字段=3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015"

共 73 条记录，以下是1-10 订阅

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Proceedings - 3rd iapr asian conference on pattern recognition, acpr 2015

Proceedings - 3rd IAPR Asian Conference on Pattern Recogniti...

引用

3rd iapr asian conference on pattern recognition, acpr 2015

ISBN: (纸本)9781479961009

The proceedings contain 164 papers. The topics discussed include: hallucination space relationship learning to improve very low resolution face recognition;robust local representation for face recognition with single sample per person;joint space learning for video-based face recognition;DeNet: an explicit distance ensemble model for person re-identification;depth-based person re-identification;structure-driven facade parsing with irregular patterns;unsupervised daily routine modeling from a depth sensor using top-down and bottom-up hierarchies;an extension of PatchMatch stereo for 3D reconstruction from multi-view images;spatial distribution feature for 3D indoor scene labeling;video-level violence rating with rank prediction;color reprint for hypochromatopsia correction;person re-identification using color enhancing feature;and online selection of discriminative features with approximated distribution fields for efficient object tracking.

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Writer identification using edge based features 3

Writer identification using edge based features

引用

3rd iapr asian conference on pattern recognition, acpr 2015

作者： Fan, Zhenyin Guo, Zhenhua Chen, Youbin Huazhong University of Science and Technology Wuhan China Graduate School at Shenzhen Tsinghua University Shenzhen China

ISBN: (纸本)9781479961009

In this paper we present a new method for writer identification, which extract original Local Binary pattern(LBP) of different radius and Edge descriptors from the edge points of the handwriting. Then, we make combinations of these edge based features. Experimental results demonstrate that the combination of edge points based features outperform traditional features extracted from the whole text, which can get state-of-the-art performance on CVL and ICDAR2013 datasets. © 2015 IEEE.

关键词： Local binary pattern

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Robust feature matching via multiple descriptor fusion 3

Robust feature matching via multiple descriptor fusion

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Hu, Yuan-Ting Lin, Yen-Yu Academia Sinica 128 Academia Road Section 2 Nankang Taipei11529 Taiwan

ISBN: (纸本)9781479961009

We present a novel approach to boost image matching performance by fusing multiple local descriptors in the homography space. Traditional matching methods find correspondences based on a single descriptor and the performance becomes unstable due to the goodness of the chosen descriptor To address this problem, our method uses multiple descriptors and select a good descriptor for matching each feature point. Specifically, we project every correspondence into the homography space, where correct correspondences tend to gather together due to the similarity of their homographies. Then kernel density estimation is applied to measure the density in the homography space and verify the correctness of correspondences. The proposed approach is comprehensively compared with the state-of-the-art methods and the promising results manifest its effectiveness. © 2015 IEEE.

关键词： pattern recognition

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Action recognition using completed local binary patterns and multiple-class boosting classifier 3

Action recognition using completed local binary patterns and...

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Yang, Yun Zhang, Baochang Yang, Linlin Chen, Chen Yang, Wankou School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical Engineering University of Texas at Dallas RichardsonTX United States School of Automation Southeast University Nanjing China

ISBN: (纸本)9781479961009

This paper, for the first time, introduces a multiple-class boosting scheme (MBS) to combine depth motion maps (DMMs) and completed local binary patterns (CLBP) for action recognition. DMMs derive from projecting depth frames onto three orthogonal Cartesian planes (front, side and top) and characterize the motion energy of an action, on which the CLBP features are further extracted. And then a new multi-class boosting method is used and leads to an effective decision-level classifier. Extensive experiments on the MSRAction3D and MSRGesture3D datasets indicate that the proposed MBS method achieves new state-of-the-art results. © 2015 IEEE.

关键词： Local binary pattern

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Reduce false positives for human detection by a priori probability in videos 3

Reduce false positives for human detection by a priori proba...

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Wang, Lei Zhao, Xu Liu, Yuncai Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University 800 Dongchuan RD Shanghai200240 China

ISBN: (纸本)9781479961009

In this work, we address the problem of reducing the false positives for human detection in videos. We employ the motion cue to build a foreground probability model. Then the mean expectation of the pixel-level foreground probability is computed to assign a priori probability to the sliding window in detection. We combine the response of Deformable Part Models and the mean probability expectation to form the features and train a linear classifier. The proposed approach is threshold-free, and reduces the false positives in human detection by the foreground cues. As well, we describe an integral probability image for fast computation of the mean probability expectation. Experimental results show that the proposed method achieve superior performance over the baseline of Deformable Part Models. © 2015 IEEE.

关键词： Probability

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Exploiting high-speed sequences for background subtraction 3

Exploiting high-speed sequences for background subtraction

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Ståhl, Nils Bergström, Niklas Ishikawa, Masatoshi Stockholm Sweden University of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781479961009

Observing the world in high speed can give rise to systems for e.g., tracking with super-human capabilities or extremely low latency human-computer interaction. However, the demand on computational power grows with the increased framerate, limiting the usefulness of such systems. In this paper we propose a background subtraction method aimed at high-speed video which enables higher level algorithms to focus on the interesting regions in the image. We show through experiments on high-speed video that where using conventional background subtraction methods in high-speed videos fail, the proposed method successfully detects the foreground in the image. © 2015 IEEE.

关键词： Human computer interaction

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Segmentation of 3D image of a rock sample supervised by 2D mineralogical image 3

Segmentation of 3D image of a rock sample supervised by 2D m...

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Igor, Varfolomeev Yakimchuk, Ivan Sharchilev, Boris Schlumberger Moscow Research Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia Moscow State University Moscow Russia

ISBN: (纸本)9781479961009

We present an approach for 2D-to-3D mineralogical information propagation and preliminary results of its implementation. An X-ray microtomography (microCT) scanner is used to obtain a 3D microstructural image of the sample. A scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) detector provides a supervising 2D mineral map of a rock sample cross section. The 2D and 3D images are then registered using a surface-based algorithm, naturally taking into account specifics of said data. The overlapping area of the images is then used as a training set for supervised mineralogical segmentation of the full 3D sample. © 2015 IEEE.

关键词： X ray detectors

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Spatial distribution feature for 3D indoor scene labelling 3

Spatial distribution feature for 3D indoor scene labelling

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Lang, Yankun Wu, Haiyuan Chen, Qian Wakayama University Sakaedani 930 Wakayama-city640-8510 Japan

ISBN: (纸本)9781479961009

In this paper, we propose an innovative approach for indoor scene labelling through a Bayesian framework with a RGB-D camera. In our approach, with the depth information, we develop a novel spatial feature vector that derives from the combination of three oriental distributions by using eigenvector decomposition and sub-space combination to capture the structural feature of 3D scene. We use Gaussian Mixture Distribution to compute the conditional likelihood density of these features. Meanwhile, a 6-connect pair-wise model that accommodates the relationship of 3D location is designed and used for labelling through Markov Random Field. Our approach is evaluated on several challenging dataset and is shown to have great superiority and effectiveness. © 2015 IEEE.

关键词： Markov processes

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Temporally coherent disparity maps using CRFs with fast 4D filtering 3

Temporally coherent disparity maps using CRFs with fast 4D f...

引用

3rd iapr asian conference on pattern recognition, acpr 2015

作者： Bigdeli, Siavash Arjomand Budweiser, Gregor Zwicker, Matthias Institute of Computer Science University of Bern Switzerland 3D Impact Media AG Switzerland

ISBN: (纸本)9781479961009

State of the art methods for disparity estimation achieve good results for single stereo frames, but temporal coherence in stereo videos is often neglected. In this paper we present a method to compute temporally coherent disparity maps. We define an energy over whole stereo sequences and optimize their Conditional Random Field (CRF) distributions using mean-field approximation. We introduce novel terms for smoothness and consistency between the left and right views, and perform CRF optimization by fast, iterative spatio-temporal filtering with linear complexity in the total number of pixels. Our results rank among the state of the art while having significantly less flickering artifacts in stereo sequences. © 2015 IEEE.

关键词： Iterative methods

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Unsupervised daily routine modelling from a depth sensor using top-down and bottom-up hierarchies 3

Unsupervised daily routine modelling from a depth sensor usi...

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3rd iapr asian conference on pattern recognition, acpr 2015

作者： Xu, Yangdi Bull, David Damen, Dima Visual Information Laboratory University of Bristol United Kingdom

ISBN: (纸本)9781479961009

A person's routine incorporates the frequent and regular behaviour patterns over a time scale, e.g. daily routine. In this work we present a method for unsupervised discovery of a single person's daily routine within an indoor environment using a static depth sensor. Routine is modelled using top down and bottom up hierarchies, formed from location and silhouette spatio-temporal information. We employ and evaluate stay point estimation and time envelopes for better routine modelling. The method is tested for three individuals modelling their natural activity in an office kitchen. Results demonstrate the ability to automatically discover unlabelled routine patterns related to daily activities as well as discard infrequent events. © 2015 IEEE.

关键词： pattern recognition

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