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An Emotional Text-Driven 3D Visual Pronunciation System for Mandarin chinese 7th

An Emotional Text-Driven 3D Visual Pronunciation System for ...

引用

7th chinese conference on pattern recognition (CCPR)

作者： Yu, Lingyun Luo, Changwei Yu, Jun Univ Sci & Technol China Dept Automat Hefei Anhui Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9789811030024;9789811030017

this paper proposes an emotional text-driven 3D visual pronunciation system for Mandarin chinese. Firstly, based on an articulatory speech corpus collected by Electro-Magnetic Articulography (EMA), the articulatory features are trained by Hidden Markov model (HMM), and the fully context-dependent modeling is taken into account by making full use of the rich linguistic features. Secondly, considering the fact that the emotion is more remarkably adjusted in the articulatory domain owing to the independency in the manipulation of articulators, the differences between articulatory movements in different emotions are investigated. thirdly, the emotional speech is generated by adjusting the speech parameters, such as fundamental frequency (F0), duration and intensity, based on Praat. then when playing the generated emotional speech, the corresponding articulatory movements are synthesized by the HMM prediction rules simultaneously which is used to drive the head mesh model along with emotional speech. the experiments demonstrate the system can synthesize accurate emotional speech synchronized animation of articulators at phoneme level.

关键词： Articulatory movement Hidden Markov model Fully context-dependent modeling Emotional speech

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Iterative Reference Driven Metric Learning for Signer Independent Isolated Sign Language recognition 14th

Iterative Reference Driven Metric Learning for Signer Indepe...

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14th European conference on computer vision (ECCV)

作者： Yin, Fang Chai, Xiujuan Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cooperat Medianet Innovat Ctr Beijing Peoples R China

ISBN: (纸本)9783319464787;9783319464770

Sign language recognition (SLR) is an interesting but difficult problem. One of the biggest challenges comes from the complex inter-signer variations. To address this problem, the basic idea in this paper is to learn a generic model which is robust to different signers. this generic model contains a group of sign references and a corresponding distance metric. the references are constructed by signer invariant representations of each sign class. Motivated by the fact that the probe samples should have high similarities with their own class references, we aim to learn a distance metric which pulls the samples and their true sign classes (references) closer and push away the samples from the false sign classes (references). therefore, given a group of references, a distance metric can be exploited with our proposed Reference Driven Metric Learning (RDML). In a further step, to obtain more appropriate references, an iterative manner is conducted to update the references and distance metric alternately with iterative RDML (iRDML). the effectiveness and efficiency of the proposed method is evaluated extensively on several public databases for both SLR and human motion recognition tasks.

关键词： Sign language recognition Signer independent Inter-signer variations Metric learning Human motion recognition

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Subspace Clustering by Capped l₁ Norm 7th

Subspace Clustering by Capped <i>l</i><sub>1</sub> Norm

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7th chinese conference on pattern recognition (CCPR)

作者： Lu, Quanmao Li, Xuelong Dong, Yongsheng Tao, Dacheng Chinese Acad Sci Xian Inst Opt & Precis Mech State Key Lab Transient Opt & Photon Ctr Opt IMagery Anal & Learning OPTIMAL Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci 19A Yuquanlu Beijing 100049 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ctr Quantum Computat & Intelligent Syst 81 Broadway St Ultimo NSW 2007 Australia

ISBN: (纸本)9789811030024;9789811030017

Subspace clustering, as an important clustering problem, has drawn much attention in recent years. State-of-the-art methods generally try to design an efficient model to regularize the coefficient matrix while ignore the influence of the noise model on subspace clustering. However, the real data are always contaminated by the noise and the corresponding subspace structures are likely to be corrupted. In order to solve this problem, we propose a novel subspace clustering algorithm by employing capped l(1) norm to deal with the noise. Consequently, the noise term with large error can be penalized by the proposed method. So it is more robust to the noise. Furthermore, the grouping effect of our method is theoretically proved, which means highly correlated points can be grouped together. Finally, the experimental results on two real databases show that our method outperforms state-of-the-art methods.

关键词： Subspace clustering Capped l(1) norm Grouping effect

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On the properties of data migration based on topology pattern keeping on cache hierarchy 7

On the properties of data migration based on topology patter...

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7th International Green and Sustainable Computing conference, IGSC 2016

作者： Hu, Jiuchuan Fan, Dongrui Li, Danping Yan, Long Ye, Xiaochun School of Computer and Information Technology of Beijing Jiaotong University Beijing100044 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Institutes of Information Engineering Chinese Academy of Science Beijing100093 China

ISBN: (纸本)9781509051175

For any data in memory, the neighborhood of data contains the relations(such as order relations) between these data that have some topology patterns. these patterns provide a base for the time or spatial locality that can be used to improve the data access latency. When the data are migrated to cache hierarchy, these topology patterns should be kept well so as to build a just in-time locality environment on the higher levels in the cache hierarchy. High quality data locality environment means high data access performance and low energy cost. thus, in this paper, we investigate the conditions under which to keep topology patterns in migrating data on-chip. © 2016 IEEE.

关键词： Topology

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Facial recognition using Histogram of Gabor Phase patterns and Self Organizing Maps 11

Facial Recognition using Histogram of Gabor Phase Patterns a...

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11th International conference on Industrial and Information Systems (ICIIS)

作者： Arya, K. V. Upadhyay, Gaurangi Upadhyay, Shambhavi Tiwari, Shailendra Sharma, Poonam ABV IIITM Gwalior Gwalior India Manipal Univ Jaipur Jaipur Rajasthan India Manipal Univ Manipal Karnataka India Thapar Univ Patiala Patiala Punjab India VNIT Nagpur Nagpur Maharashtra India

ISBN: (纸本)9781509038183

Automatic person identification using face information is very challenging task in the field of computer vision. A simple and efficient face recognition approach based on Gabor phase pattern and Self Organizing Maps is presented here. Histogram of Gabor Phase pattern is used in proposed approach for feature extraction and Self Organizing maps are used as a classifier to identify whether the given grayscale image belongs to a particular class or not. In contrast to other feature extraction techniques, faster results are obtained using Histogram of Gabor Phase patterns as feature detection occurs automatically without the need of any training procedure. A series of extensive experiments performed on various databases on MATLAB shows that proposed approach achieved a recognition rate of 97.33%, 96.49% and 94.20% for 7 consecutive trials on ORL, YALE and JAFFE databases respectively. It is demonstrated through experimental results that the proposed approach yields significant gain in terms of computational simplicity and effective accuracy.

关键词： Automatic face recognition Gabor phase pattern Histogram of Gabor Phase patterns Self Organizing Maps Sparse Auto-encoder

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A Knowledge-Based Approach to Crack Detection in thermographic Images 13th

A Knowledge-Based Approach to Crack Detection in Thermograph...

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13th International conference on Intelligent Autonomous Systems (IAS)

作者： Ghidoni, Stefano Antonello, Mauro Nanni, Loris Menegatti, Emanuele Univ Padua Dept Informat Engn I-35131 Padua Italy

ISBN: (纸本)9783319083384;9783319083377

this paper describes an innovative visual inspection system for the detection of small cracks in metal parts. Given the extremely low dimension of the defects to be detected, the system is based on a thermographic approach: defects are recognized analyzing the heat flux induced by an excitation. the system is able to analyze parts of very high complexity, like a crankshaft, thanks to the introduction of an articulated robot, used for moving the part. the system also benefits from a deep knowledge of the inspected part and of the imaging system: this is exploited to reduce the high number of artifacts and reflections that appear in thermographic images when heat sources are employed. the core of the inspection mechanism is a computer vision algorithm that is capable of analyzing the thermographic images, extract the thermal information, and exploit a Support Vector Machine (SVM) classifier to provide a final decision on the presence of a crack in the analyzed part.

关键词： computer vision Automated visual inspection thermographic image analysis pattern recognition

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Automatic Detection and Classification of Oil Tanks in Optical Satellite Images Based on Convolutional Neural Network 7th

Automatic Detection and Classification of Oil Tanks in Optic...

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7th International conference on Image and Signal Processing (ICISP)

作者： Wang, Qingquan Zhang, Jinfang Hu, Xiaohui Wang, Yang Chinese Acad Sci Inst Software Beijing Peoples R China Beijing Informat Sci & Technol Univ Beijing Peoples R China

ISBN: (纸本)9783319336183;9783319336176

Oil reserves are one of the core interests of a country. the detection of oil tanks is a very important task. So far, most studies only focus on the detection task itself. But the strategic value of different types of oil tanks is obviously different. So we furtherly divide oil tanks into two types: flat crest and cone-shaped crest. In this paper, a four-step method is adopted: (1) prepare dataset;(2) train the classifier;(3) extract candidate regions and (4) classification. the deep network (CNN) Krizhevsky used on cifar-10 dataset is used to train the classifier and ELSD is used to extract candidate regions. In addition, some clustering tricks are used to determine an only candidate region to solve the double-detection problem. the experimental results show that this method can detect and distinguish different types of oil tanks with outstanding performance.

关键词： Deep learning Convolutional Neural Network (CNN) Ellipse and Line Segment Detector (ELSD) Oil tank detection and classification Clustering

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Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark 14th

Evaluation of LBP and Deep Texture Descriptors with a New Ro...

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14th European conference on computer vision (ECCV)

作者： Liu, Li Fieguth, Paul Wang, Xiaogang Pietikainen, Matti Hu, Dewen Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China Univ Waterloo Dept Syst Design Engn Waterloo ON Canada Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Univ Oulu Ctr Machine Vis Res Oulu Finland Natl Univ Def Technol Coll Mechatron & Automat Changsha Hunan Peoples R China

ISBN: (纸本)9783319464879;9783319464862

In recent years, a wide variety of different texture descriptors has been proposed, including many LBP variants. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we propose a new extensive benchmark (RoTeB) for measuring the robustness of texture operators against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. Fourteen datasets from the eight most commonly used texture sources are used in the benchmark. An extensive evaluation of the recent most promising LBP variants and some non-LBP descriptors based on deep convolutional networks is carried out. the best overall performance is obtained for the Median Robust Extended Local Binary pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. the sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.

关键词： Local binary pattern Deep learning Performance evaluation Texture classification

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4th chinese conference on Intelligent Visual Surveillance, IVS 2016

4th Chinese Conference on Intelligent Visual Surveillance, I...

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4th chinese conference on Intelligent Visual Surveillance, IVS 2016

ISBN: (纸本)9789811034756

the proceedings contain 19 papers. the special focus in this conference is on Intelligent Visual Surveillance. the topics include: Occluded object imaging based on collaborative synthetic aperture photography;video synchronization with trajectory pulse;gestalt principle based change detection and background reconstruction;a large-scale distributed video parsing and evaluation platform;autonomous wheeled robot navigation with uncalibrated spherical images;cascaded tracking with incrementally learned projections;tracking multiple players in beach volleyball videos;multi-object tracking within air-traffic-control surveillance videos;person re-identification by multiple feature representations and metric learning;deep multi-level hashing codes for image retrieval;salient object detection from single haze images via dark channel prior and region covariance descriptor;hybrid patch based diagonal pattern geometric appearance model for facial expression recognition;multi object detection based on binocular stereo vision;fast vehicle detection in satellite images using fully convolutional network;semi-supervised hessian eigenmap for human action recognition;surveillance based crowd counting via convolutional neural networks;jet trajectory recognition based on dark channel prior and real-time abnormal behavior detection in elevator.

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A Survey of Background Modeling Based on Robust Subspace Learning via Sparse and Low-rank Matrix Decomposition 16

A Survey of Background Modeling Based on Robust Subspace Lea...

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8th International conference on Internet Multimedia Computing and Service (ICIMCS)

作者： Xie, Chaochao Yan, Yan Wang, Hanzi Lin, Lili Wang, Rui Xiamen Univ Sch Informat Sci & Technol Fujian Key Lab Sensing & Comp Smart City Fujian Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China

ISBN: (纸本)9781450348508

Moving object detection is an active and classical research topic in computer vision and pattern recognition. Background modeling based on robust subspace learning via sparse and low-rank matrix decomposition is currently one of the most popular frameworks to detect the moving objects in a video sequence, where a great lot of methods have been developed over the past few years. In this paper, we survey the background modeling methods based on robust subspace learning via sparse and low-rank matrix decomposition. We classify these robust background modeling methods in terms of different subspace learning formulation frameworks, and then analyze their respective pros and cons. Finally, we give the summary and prospects for future research.

关键词： Background modeling Robust subspace learning Moving object detection Sparse and low-rank matrix decomposition Robust principal component analysis

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