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pattern recognition 1st ed. 2016

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丛书名： Communications in computer and Information Science

2016年

作者： Tieniu Tan Xuelong Li Xilin Chen Jie Zhou Jian Yang Hong Cheng

ISBN: (数字)9789811030055

ISBN: (纸本)9789811030048

the two-volume set CCIS 662 and CCIS 663 constitutes the refereed proceedings of the 7th chinese conference on pattern recognition, CCPR 2016, held in Chengdu, China, in November 2016. the 121 revised papers presented in two volumes were carefully reviewed and selected from 199 submissions. the papers are organized in topical sections on robotics; computer vision; basic theory of pattern recognition; image and video processing; speech and language; emotion recognition.

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High Performance Large Scale Face Recog 16

High Performance Large Scale Face Recog

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16th IEEE International conference on computer vision Workshops, ICCVW 2017

作者： Xu, Yan Cheng, Yu Zhao, Jian Wang, Zhecan Xiong, Lin Jayashree, Karlekar Tamura, Hajime Kagaya, Tomoyuki Pranata, Sugiri Shen, Shengmei Feng, Jiashi Xing, Junliang Panasonic RandD Center Singapore Singapore National University of Singapore Singapore Franklin. W. Olin College of Engineering United States Panasonic Corporation Japan National Laboratory of Pattern Recognition Panasonic RandD Center Singapore Institute of Automation Chinese Academy of Sciences Panasonic RandD Center Singapore Japan

ISBN: (纸本)9781538610343

In this paper, we introduce our solution to the Challenge-1 of the MS-Celeb-lM challenges which aims to recognize one million celebrities. To solve this large scale face recognition problem, a Multi-Cognition Softmax Model (MCSM) is proposed to distribute training data to several cognition units by a data shuffling strategy. Here we introduce one cognition unit as a group of independent softmax models, which is designed to increase the diversity of the one softmax model to boost the performance for models ensemble. Meanwhile, a template-based Feature Retrieval (FR) module is adopted to improve the performance of MCSM by a specific voting scheme. Moreover, a one-shot learning method is applied on collected extra 600K identities due to each identity has one image only. Finally, testing images with lower score from MCSM and FR are assigned new labels with higher score by merging one-shot learning results. Extensive experiments on the MS-Celeb-1M testing set demonstrate the superiority of the proposed method. Our solution ranks the first place in both two settings of the final evaluation and outperforms other teams by a large margin. © 2017 IEEE.

关键词： Face recognition

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Efficient Audio-Visual Speaker recognition via Deep Heterogeneous Feature Fusion 12th

Efficient Audio-Visual Speaker Recognition via Deep Heteroge...

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12th chinese conference on Biometric recognition, CCBR 2017

作者： Liu, Yu-Hang Liu, Xin Fan, Wentao Zhong, Bineng Du, Ji-Xiang Department of Computer Science Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China

ISBN: (纸本)9783319699226

Audio-visual speaker recognition (AVSR) has long been an active research area primarily due to its complementary information for reliable access control in biometric system, and it is a challenging problem mainly attributes to its multimodal nature. In this paper, we present an efficient audio-visual speaker recognition approach via deep heterogeneous feature fusion. First, we exploit a dual-branch deep convolutional neural networks (CNN) learning framework to extract and fuse the high-level semantic features of face and audio data. Further, by considering the temporal dependency of audio-visual data, we embed the fused features into a bidirectional Long Short-Term Memory (LSTM) networks to produce the recognition result, though which the speakers acquired under different challenging conditions can be well identified. the experimental results have demonstrated the efficiency of our proposed approach in both audio-visual feature fusion and speaker recognition. © 2017, Springer International Publishing AG.

关键词： Long short-term memory

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Autonomous Object Segmentation in Cluttered Environment through Interactive Perception 7th

Autonomous Object Segmentation in Cluttered Environment Thro...

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7th International conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Wu, Rui Zhao, Dongfang Liu, Jiafeng Tang, Xianglong Huang, Qingcheng Harbin Inst Technol Res Ctr Pattern Recognit & Intelligent Syst Harbin Heilongjiang Peoples R China

ISBN: (纸本)9783319677774;9783319677767

this paper investigates the problem of object segmentation in cluttered environment. this problem enables a large variety of exciting and important applications. An interactive perception method is proposed to segment scene into constituent objects based on principal angle. Trajectory data of feature points reflecting the essence of scene structure changes is extracted by a robot arm to calculate least stable regions for the interactive task. the segmentation task is achieved by the principal angle of stable regions because the principal angle essentially estimates the similarity between two regions. In contrast to probability based approach, our method performs well on efficiency of segmentation as it works without estimating parameters of the system. Experimental results on real world scene confirm the effectiveness of our method.

关键词： Object segmentation Interactive perception Trajectory Principal angle

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Pose-Based Temporal-Spatial Network (PTSN) for Gait recognition with Carrying and Clothing Variations 12th

Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognit...

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12th chinese conference on Biometric recognition, CCBR 2017

作者： Liao, Rijun Cao, Chunshui Garcia, Edel B. Yu, Shiqi Huang, Yongzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China 7ma A 21406 PlayaHavana Cuba

ISBN: (纸本)9783319699226

One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. the body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions. © 2017, Springer International Publishing AG.

关键词： Gesture recognition

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EEG-Based Motor Imagery Differing in Task Complexity 7th

EEG-Based Motor Imagery Differing in Task Complexity

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7th International conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Liu, Kunjia Yu, Yang Liu, Yadong Zhou, Zongtan Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783319677774;9783319677767

In this study, we explored the classification of singlehanded motor imagery (MI) EEG signals with different complexity. Eight healthy participants were asked to complete a finger-tapping task of different complexity. In signal processing, CSP features were extracted from the band-passed EEG signals. then, these features were used to define a score using the step-wise linear discriminant analysis (SWLDA) method. the classification accuracy was evaluated by a five-fold cross-validation strategy. the experimental results showed that the average accuracy between different complexity is 79.20%, and the highest is up to 80.84%, indicating the separability of EEG-based MI tasks with different complexity. the EEG-based complexity distinction achieved in this paper would encourage further study of the realization of multiclass MI-based BCI paradigm.

关键词： Brain-computer interface (BCI) Electroencephalogram (EEG) Motor imagery Task complexity Common spatial pattern (CSP)

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Stereo matching algorithm based on the combination of matching costs 7

Stereo matching algorithm based on the combination of matchi...

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7th IEEE Annual International conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

作者： Ende, Wang Yalong, Zhu Liangyu, Peng Yijun, Li Tianyao, Wu Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing CAS Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China College of Electrical Engineering Yanshan University Qinhuangdao066004 China School of Metallurgy Northeastern University Shenyang110819 China

ISBN: (纸本)9781538604892

Aiming at solving the problem of low accuracy of weak texture region and disparity discontinuous region and sensitivity to illumination caused by existing local stereo matching algorithm, a new stereo matching algorithm based on the combination of Sum of Absolute Difference algorithm and Absolute Difference of Gradient algorithm is presented here. Using the average of the pixels in the disparity range to apply adaptive weight to the SAD part. the new algorithm can adaptively adjust each part of the cost function in the proportion of the total cost function according to the different texture, illumination characteristics of image. Moreover, we use the semiglobal matching algorithm to calculate the cost of 8 directions, to carry out optimized processing on disparity image, finally a dense disparity map is obtained. the experimental results shows that the proposed method not only improve accuracy of stereo matching, but also has well results in weak texture region and disparity discontinuous region. © 2017 IEEE.

关键词： Textures

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Max-Feature-Map Based Light Convolutional Embedding Networks for Face Verification 12th

Max-Feature-Map Based Light Convolutional Embedding Networks...

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12th chinese conference on Biometric recognition, CCBR 2017

作者： Yang, Zhou Jian, Meng Bao, Bingkun Wu, Lifang Faculty of Information Technology Beijing University of Technology Beijing China Nanjing Jingjunhai Network Ltd. NanjingJiangsu China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319699226

the powerful image feature extraction ability of convolutional neural network makes it possible to achieve great success in the field of face recognition. However, this category of models tend to be deep and paralleled which is not capable to be applied in real-time face recognition tasks. In order to improve its feasibility, we propose a max-feature-map activation based fully convolutional structure to extract face features with higher speed and less computational cost. the learned model has a great potential on embedding in the hardware devices due to its high recognition performance and small storage space. Experimental results demonstrate that the proposed model is 63 times smaller in comparison with the famous VGG model. At the same time, 96.80% verification accuracy is achieved for a single network on LFW benchmark. © 2017, Springer International Publishing AG.

关键词： Feature extraction

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Multi-region ensemble convolutional neural networks for high-accuracy age estimation 28

Multi-region ensemble convolutional neural networks for high...

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28th British Machine vision conference, BMVC 2017

作者： Chen, Yiliang Tan, Zichang Leung, Alex Po Wan, Jun Zhang, Jianguo Faculty of Information Technology Macau University of Science and Technology Macau SAR China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Computing School of Science and Engineering University of Dundee United Kingdom

ISBN: (纸本)190172560X

In real life, when telling a person’s age from his/her face, we tend to look at his/her whole face first and then focus on certain important regions like eyes. After that we will focus on each particular facial feature individually like the nose or the mouth so that we can decide the age of the person. Similarly, in this paper, we propose a new framework for age estimation, which is based on human face sub-regions. Each sub-network in our framework takes the input of two images each from human facial region. One of them is the global face, and the other is a vital sub-region. then, we combine the predictions from different sub-regions based on a majority voting method. We call our framework Multi-Region Network Prediction Ensemble (MRNPE) and evaluate our approach using two popular public datasets: MORPH Album II and Cross Age Celebrity Dataset (CACD). Experiments show that our method outperforms the existing state-of-the-art age estimation methods by a significant margin. the Mean Absolute Errors (MAE) of age estimation are dropped from 3.03 to 2.73 years on the MORPH Album II and 4.79 to 4.40 years on the CACD. © 2017. the copyright of this document resides with its authors.

关键词： computer vision

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Digital Image Patch Based Randomized Crack Detection in Concrete Structures 7

Digital Image Patch Based Randomized Crack Detection in Conc...

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7th International conference on computer Communication and Informatics (ICCCI)

作者： Sharma, Mayank Sharma, Mahesh Kumar Leeprechanon, Nopbhorn Anotaipaiboon, Weerachai Chaiyasarn, Krisada Thammasat Univ Dept Elect & Comp Engn Pathum Thani 12120 Thailand Thammasat Univ Dept Civil Engn Pathum Thani 12120 Thailand

ISBN: (纸本)9781467388566;9781467388559

Assessment of aging civil infrastructure should be done periodically to getting information about the structural condition. In context to it, classification, detection, and localization of cracks within these concrete structures is of paramount importance. the most commonly used procedure, i.e. visual inspection, is executed manually by human inspectors, and thus, its accuracy depends on to this inspection procedure. Apart from it, this approach is quite labor-intensive and time-consuming, resulting in short inspection cycles. By taking this into consideration, an automatic supervised learning-based crack detection method has been presented in this paper employing digital images of concrete structures as samples, thereby providing assistance to the inspection process extensively. In this method, images segmentation operation has been achieved using the MATLAB (R) Color thresholder App to distinguish crack free and non-crack free regions as being a pre-processing step. In addition, the manual labeling operation has also been exploited to assign binary class labels to each data set image patches following the overlay operation. Random Forests, a combination of bagging and random subspace algorithms, has been employed and trained by inputting original RGB Training image patches along with the ground truth binary class labels to perform prediction on various concrete surface test images provided without any labels whether it belongs to a crack class or not. From the obtained experimental results, it is envisaged that the proposed method is robust enough to identify cracks in concrete structures. Here, receiver operating characteristic (ROC) analysis has also been applied to investigate the performance of the proposed method provided in terms of the performance metric.

关键词： computer vision concrete structures crack detection image processing machine learning pattern classification random decision forests segmentation thresholding

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