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A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting recognition

A Weighted Finite-State Transducer (WFST)-Based Language Mod...

引用

International Conference on Document Analysis and recognition

作者： Suhan Chowdhury Utpal Garain Tanushyam Chattopadhyay Computer Vision & Pattern Recognition (CVPR) Unit Indian Statistical Institute Kolkata India Innovation Laboratory Kolkata Tata Consultancy Services Kolkata India

Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

关键词： Handwriting recognition Hidden Markov models Character recognition Optical character recognition software Accuracy Transducers Vocabulary

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Multi-color ULBP with wavelet transform in invariant pose face recognition

Multi-color ULBP with wavelet transform in invariant pose fa...

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IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Seyed Omid Shahdi S. A. R. Abu-Bakar Computer Vision Video and Image Processing (CvviP) Research Lab Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Variation in pose is one of the main obstacles confronting researchers in the area of face recognition. In this paper, a novel method is proposed to explicitly tackle this problem. Multi-color uniform local binary pattern (ULBP) is introduced for extracting salient features along with wavelet transform. Learning scheme is adopted to obtain a mapping coefficient vector between face in a pose and frontal face. Then expected frontal face view vector could be generated by inserting the posed face. Instead of using the entire face, some of its important regions are taken into account. The proposed method relies only on single frontal face image as a gallery image. Results have demonstrated that the proposed method operates well even under the low-resolution conditions.

关键词： Face Vectors Face recognition Feature extraction Wavelet transforms Conferences

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Continuous emotion detection in response to music videos

Continuous emotion detection in response to music videos

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International Conference on Automatic Face and Gesture recognition

作者： Mohammad Soleymani Sander Koelstra Ioannis Patras Thierry Pun Computer Vision and Multimedia Laboratory University of Genova Switzerland School of Computer Science and Electronic Engineering Queen''s Mary University of London UK

Viewers' preference for multimedia selection depends highly on their emotional experience. In this paper, we present an emotion detection method for music videos using central and peripheral nervous system physiological signals as well as multimedia content analysis. A set of 40 music clips eliciting a broad range of emotions were first selected. After extracting the one minute long emotional highlight of each video, they were shown to 32 participants while their physiological responses were recorded. Participants self-reported their felt emotions after watching each clip by means of arousal, valence, dominance, and liking ratings. The physiological signals included electroencephalogram, galvanic skin response, respiration pattern, skin temperature, electromyograms and blood volume pulse using plethysmograph. Emotional features were extracted from the signals and the multimedia content. The emotional features were used to train a linear ridge regressor to detect emotions for each participant using a leave-one-out cross-validation strategy. The performance of the personalized emotion detection is shown to be significantly superior to a random regressor.

关键词： Videos Feature extraction Electroencephalography Skin Multimedia communication Heart rate variability Electrodes

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Multi-Level Kernel Machine for Scene Image Classification

Multi-Level Kernel Machine for Scene Image Classification

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International Conference on Computational Intelligence and Security

作者： Junlin Hu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.

关键词： Kernel Computed tomography Transforms computer vision Histograms Conferences Support vector machines

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Neural network based motion segmentation for accelerometer applications

Neural network based motion segmentation for accelerometer a...

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IEEE Symposium on 3D User Interfaces (3DUI)

作者： Jong Gwan Lim Sang-Youn Kim Dong-Soo Kwon KAIST South Korea Korea Advanced Institute of Science and Technology Korea University of Technology and Education South Korea

Of several research issues related to motion interaction using inertia measurement units, faster motion segmentation without accuracy loss has recently been raised. Instead of using excessive filtering that produces time delay or tricky use of multiple thresholds that cause difficulty in parameter optimization, this poster demonstrates that time series prediction using neural networks significantly decreases time delay and guarantees rigid motion segmentation by detecting end points in accelerometer signals. According to a general pattern recognition procedure, feature selection is made by a filtering method and the optimal structure is determined by cross validation. Radial basis function networks and Multi-Layer Perceptrons (MLPs) are tested and the results are compared with the conventional methods to evaluate accuracy and time delay in a handwriting case in 3D space. This study confirms that MLP shows the best accuracy and shortens the time delay by 1/4~1/3 compared to the conventional methods.

关键词： Delay effects Acceleration Accelerometers Motion segmentation computer vision pattern recognition Accuracy

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針對盲人輔助應用之電腦視覺演算法開發與硬體架構設計

針對盲人輔助應用之電腦視覺演算法開發與硬體架構設計

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作者：李嘉祥臺灣大學

学位级别：碩士

近年来，在盲人辅助系统上，电脑视觉分析逐渐变为主流研究方向，在现阶段中，最普遍使用的盲人辅助系统为导盲犬与盲人手杖，然而，此种方式包含许多缺点如不方便性及价格昂贵等，在社交的场合或是拥挤的环境中，此种辅助方式极为不适... 详细信息

近年来，在盲人辅助系统上，电脑视觉分析逐渐变为主流研究方向，在现阶段中，最普遍使用的盲人辅助系统为导盲犬与盲人手杖，然而，此种方式包含许多缺点如不方便性及价格昂贵等，在社交的场合或是拥挤的环境中，此种辅助方式极为不适用。此外，当盲人在行走於户外环境时，它们无法提供环境的分析与障碍物的侦测，相较之下，电子辅助系统是较为理想的解决方式，它们提供了更多环境资讯与导航能力。然而最新的电子辅助系统较为笨重且功能受到限制，在本篇论文中，我们设计了携带式视觉分析盲人辅助系统与视觉分析导航系统，此系统帮助盲人行走并提供重要之环境资讯。首先，我们发展出智慧型以深度图(Depth Map)为基准之障碍物侦测系统，此系统的高准确度使得盲人可以快速且轻易闪避障碍物，根据深度图的切割(Segmentation)与空间分布关系，我们提出的演算法可以高达95%以上的准确，当障碍物被抓取之後，系统会提供障碍物的资讯例如深度、方向等，更进一步使用物体追踪演算法(Object Tracking)来记录障碍物之方向与路径的轨迹，藉此可以预测障碍物未来动线以提早闪避，接着第二部份，为了使盲人了解环境资讯，我们提出深度特性模型(Depth Characteristic Mode)来抽取环境资讯，例如：道路、楼梯与墙壁，此功能可以使得盲人能更了解周遭环境，抽取出这些特定的资讯来使得盲人安全的行走道路、攀爬楼梯、沿着墙壁行走等，在第三部份更透过结合图形辨识功能(pattern recognition)，设计出以全球卫星定位系统GPS(Globe Positioning System)为基准之视觉导航系统(vision-based Navigation System)，这使得盲人可以在陌生的环境中，更精确定位所在的位置，借此我们可以达到更精确的导航功能，在论文最後，我们挑出了两项在系统架构中最为耗时的区块来做硬体加速，使得此系统可以达到即时侦测的功能，此两区块分别为图形切割与型态学操作(Morphology)，我们所用的制程为台积电(TSMC)提供之 65 奈米(nm)制程，此晶片可以操作在200MHz 之频率，所耗的功率为56 毫瓦(mW)，透过我们的硬体架构设计，在解析度640*480 之下可以达到2000 张每秒的处理速度，在最为复杂的情况下，也可以有28 张每秒的处理速度。总合来说，我们提出了一套的盲人辅助系统之演算法与硬体架构设计，其中包含三大部分软体演算法，分别为障碍物侦测、深度特性模型侦测与视觉导航系统，并且对於其中运算量最高之部份做硬体加速，在65 奈米制程下可使得整体系统可以达到即时侦测之功能。

关键词：盲人輔助系電腦視覺深度圖物體追蹤圖形辨識視覺導航系統深度特性模型硬體架構設計型態學操作

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Robust upper body detection in unconstrained posture

Robust upper body detection in unconstrained posture

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International Conference on Multimedia Technology

作者： Diping Li Beiji Zou Department of Math & Information Technology Hanshan Normal University Chaozhou China School of Information Science and Engineering Central South University Changsha China

ISBN: (纸本)9781612847719

This paper presents an efficient approach to detecting upper body in unconstrained posture based on Shape Context and Histograms of Orient Gradients. The method contains two steps. Shape context matching is used to get the candidates of upper bodies in the images at first, and then validation of each candidate is performed by using the Histograms of Orient Gradients. The method has two advantages, comparing with existing body detection methods. One is that it can detect upper bodies in all kinds of postures other than upright standing posture, and the other is that it significantly speeds up the computation.

关键词： Shape Context Humans Histograms computer vision Support vector machines pattern recognition

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Interclass Visual Similarity Based Visual Vocabulary Learning

Interclass Visual Similarity Based Visual Vocabulary Learnin...

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第一届亚洲模式识别会议

作者： Guangming Chang Chunfen Yuan Weiming Hu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beij National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of SciencesBeijin

Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass visual similarity statistically and then use it to supervise the generation of visual vocabulary. We construct a measurement of interclass similarity, embed the similarity to the Euclidean distance and use the refined distance to generate visual vocabulary iteratively. The experiments in Weizmann and KTH datasets show that our approach outperforms the traditional vocabulary based approach by about 5％.

关键词： Vocabulary Visualization Clustering algorithms pattern recognition Conferences Vectors computer vision

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Classification of facial expressions by sparse coding

Classification of facial expressions by sparse coding

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IEEE Signal Processing and Communications Applications (SIU)

作者： Nesli Erdoğmuş Jean-Luc Dugelay Multimedia Communications Department Eurecom Institute Sophia-Antipolis France

Expression variations in facial images is one of the most crucial and difficult problems in face-based computer vision applications. Although numerous systems have been proposed for robustness against facial expressions, so far it still persists to be an open *** that the knowledge on the type of the expression in a facial image would greatly facilitate the solution of this issue, in this paper we present an analysis for facial expressions classification in 2D frontal views. With the motivation of the success that sparse coding achieved in face recognition, similar principals are applied for to both original and dimension-reduced (via PCA) images and the resulting codes are classified based on two different approaches: minimum residual error and maximum interclass summation of the coefficients. Extensive tests are conducted on Bosphorus database, in which different expressions are available for 105 persons.

关键词： Conferences Face recognition Face Signal processing pattern analysis Principal component analysis

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Detect saliency to understand a photo

Detect saliency to understand a photo

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International Conference on Multimedia Technology

作者： Lin Cong Ruo-Feng Tong Da-Yong Bao College of Computer Science and Technology University of Zhejiang Hangzhou Zhejiang China

Visual saliency is an extremely hot topic in image and video processing. Our paper addresses a novel method for saliency detection, which conveys the photographers' idea of a scene. It consists of analyzing the photo elements like color, depth-of-field (DOF) and composition. We use the color spatial distribution feature to calculate the initial saliency. A simple but efficient classifier is trained to discriminate the low DOF image. For this kind of images we find the focal area to refine the initial saliency. The saliency map will be further adjusted by an image composition analysis step to generate the final output. We compare our framework with state-of-the-art algorithms. Experimental results show that, our method out-performs the previous methods in many cases, especially in the low DOF situation.

关键词： Image color analysis Visualization Image edge detection Conferences computer vision pattern recognition Computational modeling

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