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LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression recognition 12

引用

12th Asian conference on computer vision (ACCV)

作者： Wang, Yandan See, John Phan, Raphael C. -W. Oh, Yee-Hui Multimedia Univ MMU Fac Engn Cyberjaya Malaysia Multimedia Univ MMU Fac Comp & Informat Cyberjaya Malaysia

ISBN: (纸本)9783319168654;9783319168647

Facial micro-expression recognition is an upcoming area in computer vision research. Up until the recent emergence of the extensive CASMEII spontaneous micro-expression database, there were numerous obstacles faced in the elicitation and labeling of data involving facial micro-expressions. In this paper, we propose the Local Binary patterns with Six Intersection Points (LBP-SIP) volumetric descriptor based on the three intersecting lines crossing over the center point. the proposed LBP-SIP reduces the redundancy in LBP-TOP patterns, providing a more compact and lightweight representation;leading to more efficient computational complexity. Furthermore, we also incorporated a Gaussian multi-resolution pyramid to our proposed approach by concatenating the patterns across all pyramid levels. Using an SVM classifier with leaveone- sample-out cross validation, we achieve the best recognition accuracy of 67.21 %, surpassing the baseline performance with further computational efficiency.

关键词： Local binary pattern

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Face Detection Based on Multi-block Quad Binary pattern 1

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12th Asian conference on computer vision (ACCV)

作者： Ge, Zhubei Cai, Canhui Zeng, Huanqiang Zhu, Jianqing Ma, Kai-Kuang Huaqiao Univ Sch Informat Sci & Engn Quanzhou Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Nanyang Technol Univ Sch EEE Singapore 639798 Singapore

ISBN: (数字)9783319166285

ISBN: (纸本)9783319166285;9783319166278

A novel local texture descriptor, called multi-block quad binary pattern (MB-QBP), is proposed in this paper. To demonstrate its effectiveness on local feature representation and potential usage in computer vision applications, the proposed MB-QBP is applied to face detection. Compared with the multi-block local binary pattern (MB-LBP), MB-QBP has more features to conduct a better training process to refine the classifier. Consequently, the over-fitting problem becomes much smaller in the MB-QBP-based classifier. Extensive simulation results conducted by using the test images from the BioID and CMU+MIT databases have clearly shown that the proposed MB-QBP-based face detector outperforms the MB-LBP-based approach by about 6% on the correct detection rate under the same training conditions.

关键词： Face recognition

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Overview of Topic-based chinese message polarity classification in SIGHAN 2015 8

Overview of Topic-based Chinese message polarity classificat...

引用

8th SIGHAN Workshop on chinese Language Processing, SIGHAN 2015

作者： Liao, Xiangwen Li, Binyang Xu, Liheng College of Mathematics and Computer Science Fuzhou University China School of Information Science and Technology University of International Relations China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781941643570

this paper presents the overview of Topic-based chinese Message Polarity Classification in SIGHAN 2015 bake-off. Topic-based message polarity classification plays an important role in sentiment analysis, information extraction, event tracking, and other related research areas. this task is designed to evaluate the techniques for chinese message polarity classification towards a given topic. the task organizers manually constructed 25 topics together with 24,374 corresponding messages which were annotated to construct the training and testing datasets. the evaluation results achieved by the participators provide good suggestion for the future research. © 2015 Proceedings of the 8th SIGHAN Workshop on chinese Language Processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint conference on Natural Language Processing, ACL IJCNLP 2015. All rights reserved.

关键词： Sentiment analysis

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Automatic detection of social interactions in the waiting area of automatic milking stations using a video surveillance system 7

Automatic detection of social interactions in the waiting ar...

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7th European conference on Precision Livestock Farming, ECPLF 2015

作者： Guzhva, O. Ardö, H. Herlin, A. Nilsson, M. Aström, K. Bergsten, C. Swedish University of Agricultural Sciences Department of Biosystems and Technology Alnarp Sweden Center for Mathematical Sciences Lund University Lund Sweden

ISBN: (纸本)9788890975325

A well-planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete to enter the milking station and are exposed to a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. the aim was to monitor the waiting area in a free stall dairy by means of three video cameras in order to detect the occurrence of social interactions using improved image segmentation and tracking methods. the surveillance system observed 250 cows with free access to any of four milking stations during 24 hours over a period of two weeks. A two-step pattern recognition approach was used. In the first step geometric features (distances) were extracted from every pair of cows in every frame. these features form the input to the second step in which a cow behaviour classifier was developed. A support vector machine was used to create this classifier. the social interactions were identified based on collision of geometrical shapes segmented from the image and positively identified as cows by experienced observers. the results showed that the proposed system was capable of reasonably accurate detection of social interactions.

关键词： computer vision

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Information Based Universal Feature Extraction 7

Information Based Universal Feature Extraction

引用

7th International conference on Machine vision (ICMV)

作者： Amiri, Mohammad Brause, Ruediger Goethe Univ Frankfurt Dept Comp Sci D-60054 Frankfurt Germany

ISBN: (纸本)9781628415605

In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. the goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. this will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

关键词： Machine vision Universal feature extraction Information theory Transfer learning

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Cross Dataset Person Re-identification 12th

Cross Dataset Person Re-identification

引用

12th Asian conference on computer vision (ACCV)

作者： Hu, Yang Yi, Dong Liao, Shengcai Lei, Zhen Li, Stan Z. Chinese Acad Sci CASIA Inst Automat Ctr Biometr & Secur Res Natl Lab Pattern Recognit Beijing 100190 Peoples R China

ISBN: (纸本)9783319166346;9783319166339

Until now, most existing researches on person re-identification aim at improving the recognition rate on single dataset setting. the training data and testing data of these methods are form the same source. Although they have obtained high recognition rate in experiments, they usually perform poorly in practical applications. In this paper, we focus on the cross dataset person re-identificationwhich make more sense in the real world. We present a deep learning framework based on convolutional neural networks to learn the person representation instead of existing hand-crafted features, and cosine metric is used to calculate the similarity. three different datasets Shinpuhkan2014dataset, CUHK and CASPR are chosen as the training sets, we evaluate the performances of the learned person representations on VIPeR. For the training set Shinpuhkan2014dataset, we also evaluate the performances on PRID and iLIDS. Experiments show that our method outperforms the existing cross dataset methods significantly and even approaches the performances of some methods in single dataset setting.

关键词： Deep learning

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Shape Matching Using Point Context and Contour Segments 1

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12th Asian conference on computer vision (ACCV)

作者： Feinen, Christian Yang, Cong Tiebe, Oliver Grzegorzek, Marcin Univ Siegen Dept ETI Res Grp Pattern Recognit D-57076 Siegen Germany

ISBN: (数字)9783319168173

ISBN: (纸本)9783319168173;9783319168166

this paper proposes a novel method to generate robust contour partition points and applies them to produce point context and contour segment features for shape matching. the main idea is to match object shapes bymatching contour partition points and contour segments. In contrast to typical shape context method, we do not consider the topological graph structure since our approach is only considering a small number of partition points rather than full contour points. the experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations. the most significant scientific contributions of this paper include (i) the introduction of a novel partition point extraction technique for point context and contour segments as well as (ii) a new fused similarity measure for object matching and recognition, and (iii) the impressive robustness of the method in an object retrieval scenario as well as in a real application for environmental microorganism recognition.

关键词： Topology

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60 Hz self-tuning background modeling 15

60 Hz self-tuning background modeling

引用

7th International conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Luo, Jun Wang, Jinqiao Zhang, La Chen, Yingying Xu, Huazhong Lu, Hanqing School of Automation Wuhan University of Technology Wuhan430070 China National Laboratory of Pattern Recognition CASIA Beijing100190 China

ISBN: (纸本)9781450335287

Background modeling or change detection is often used as a preprocessing step in many computer vision tasks especially for intelligent surveillance. Despite various methods have been proposed to deal with this problem, they often involve complex parameter settings and have poor adaptability to scene changes. In this paper, we propose a fast and robust approach for background modeling with self-adaptive ability. Like ViBe [7], each pixel model is represented by a sequence of historical samples based on sample consensus. To adapt various changes in complex scenes, a flexible feedback scheme is presented to automatically adjust the model parameters. Moreover, a selective diffusion method is employed to overcome the problems like incomplete foregrounds or false detections brought by intermittent moving objects. Experiment results on Change Detection benchmark 2014 show that the proposed approach outperforms state-of-theart approaches with a speed of 60 fps on CPU for a 640×480 image sequence. © 2015 ACM.

关键词： Change detection

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Fast and Robust Algorithm for Fundamental Matrix Estimation 12th

Fast and Robust Algorithm for Fundamental Matrix Estimation

引用

12th International conference on Image Analysis and recognition (ICIAR)

作者： Zhang, Ming Wang, Guanghui Chao, Haiyang Wu, Fuchao Univ Kansas Sch Engn Lawrence KS 66045 USA Chinese Acad Sci Natl Lab Pattern Recognit Beijing 100190 Peoples R China

ISBN: (纸本)9783319208015;9783319208008

Fundamental matrix estimation from two views plays an important role in 3D computer vision. In this paper, a fast and robust algorithm is proposed for the fundamental matrix estimation in the presence of outliers. Instead of algebra error, the reprojection error is adopted to evaluate the confidence of the fundamental matrix. Assuming Gaussian image noise, it is proved that the reprojection error can be described by a chi-square distribution, and thus, the outliers can be eliminated using the 3-sigma principle. With this strategy, the inlier set is robustly established in only two steps. Compared to classical RANSAC-based strategies, the proposed algorithm is very efficient with higher accuracy. Experimental evaluations and comparisons with previous methods demonstrate the effectiveness and advantages of the proposed approach.

关键词： Fundamental matrix Robust estimation Outlier elimination

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Towards Practical Face recognition: A Local Binary pattern Non Frontal Faces Filtering Approach 10th

Towards Practical Face Recognition: A Local Binary Pattern N...

引用

10th chinese conference on Biometric recognition (CCBR)

作者： Zhai, Yikui Wang, Xiaolin Gan, Junying Xu, Ying Wuyi Univ Sch Informat Engn Jiangmen Peoples R China

ISBN: (纸本)9783319254173;9783319254166

In dynamic real-time face detection and recognition system, the non frontal faces with different tilt and deflection pose has great influence on the recognition accuracy, in order to solve these problems, we propose non frontal faces filter's method via support vector machine(SVM) and local binary patterns(LBP). By this method the images with large pose deflection will be filtered. Firstly, we apply the AdaBoost algorithm into real-time face detection and join the nose detection to further filter non face images. then we extract texture feature from the detected face images by LBP feature operator. Finally, SVM is used to classify frontal and non frontal faces. Experimental results show that the proposed method has good classification capability for face images with varying pose. It contribute to eliminate the impact of pose variation in dynamic face recognition system.

关键词： Face recognition LBP Pose classification SVM

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