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Trajectory-based scene understanding using dirichlet process mixture model

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arXiv 2018年

作者： Santhosh, Kelathodi Kumaran Dogra, Debi Prosad Roy, Partha Pratim Chaudhuri, Bidyut Baran School of Electrical Sciences Indian Institute of Technology Bhubaneswar Odisha India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India Techno India University Saltlake city Sector V Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unusual routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions in road marking road can be avoided. In this paper, we propose an unsupervised and nonparametric method to learn frequently used paths from the tracks of moving objects in Θ(kn) time, where k denotes the number of paths and n represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are considered to make the clustering meaningful using Temporally Incremental Gravity Model (TIGM). In addition, the distance-based scene learning makes it intuitive to estimate the model parameters. Further, we have extended TIGM hierarchically as Dynamically Evolving Model (DEM) to represent notable traffic dynamics of a scene. Experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths (k). We have compared the results with various state-of-the-art methods. We have also highlighted the advantages of the proposed method over existing techniques popularly used for designing traffic monitoring applications. It can be used for administrative decision making to control traffic at junctions or crowded places and generate alarm signals, if necessary. Copyright © 2018, The Authors. All rights reserved.

关键词： Intelligent systems

A new cold feature based handwriting analysis for enthnicity/nationality identification

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arXiv 2018年

作者： Nag, Sauradip Shivakumara, Palaiahnakote Yirui, Wu Pal, Umapada Lu, Tong Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, explores tangent angle for the contour pixels in each row and the mean of intensity values of each row in an image for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method. Copyright © 2018, The Authors. All rights reserved.

关键词： Crime

ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on pattern recognition (ICPR) 2016 and International Conference on computer vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

New COLD Feature Based Handwriting Analysis for Enthnicity/Nationality Identification

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New COLD Feature Based Handwriting Analysis for Enthnicity/N...

International Workshop on Frontiers in Handwriting recognition

作者： Sauradip Nag Palaiahnakote Shivakumara Yirui Wu Umapada Pal Tong Lu Kalyani Government Engineering College Kalyani Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia College of Computer and Information Hohai University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China

Identifying crime for forensic investigating teams when crimes involve people of different nationals is challenging. This paper proposes a new method for ethnicity (nationality) identification based on Cloud of Line Distribution (COLD) features of handwriting components. The proposed method, at first, uses tangent angle of the contour pixels in each row and the mean of intensity values of each row for segmenting text lines. For segmented text lines, we use tangent angle and direction of base lines to remove rule lines in the image. We use polygonal approximation for finding dominant points for contours of edge components. Then the proposed method connects the nearest dominant points of every dominant point, which results in line segments of dominant point pairs. For each line segment, the proposed method estimates angle and length, which gives a point in polar domain. For all the line segments, the proposed method generates dense points in polar domain, which results in COLD distribution. As character component shapes change, according to nationals, the shape of the distribution changes. This observation is extracted based on distance from pixels of distribution to Principal Axis of the distribution. Then the features are subjected to an SVM classifier for identifying nationals. Experiments are conducted on a complex dataset, which show the proposed method is effective and outperforms the existing method.

关键词： Feature extraction Shape Image edge detection Writing Image segmentation Forensics Support vector machines

Sclera vessel pattern synthesis based on a non-parametric texture synthesis technique

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Sclera vessel pattern synthesis based on a non-parametric te...

International Conference on computer vision and Image Processing, CVIP 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Blumenstein, Michael Ferrer, Miguel A. Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9789811021060

This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers. © Springer Science+Business Media Singapore 2017.

关键词： Biometrics pattern Sclera Synthesis Texture

FWLBP: A scale invariant descriptor for texture classification

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arXiv 2018年

作者： Roy, Swalpa Kumar Bhattacharya, Nilavra Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Optical Character Recognition Laboratory Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India School of Information University of Texas AustinTX78712 United States Image Processing Laboratory Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors. Copyright © 2018, The Authors. All rights reserved.

关键词： Fractal dimension

Indic handwritten script identification using offline-online multi-modal deep network

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arXiv 2018年

作者： Bhunia, Ayan Kumar Mukherjee, Subham Sain, Aneeshan Bhunia, Ankan Kumar Roy, Partha Pratim Pal, Umapada Institute for Media Innovation Nanyang Technological University Singapore Centre for Vision Speech and Signal Processing University of Surrey England United Kingdom Department of ECE Institute of Engineering & Management Kolkata India Department of EE Institute of Engineering & Management Kolkata India Department of Electrical Engineering Jadavpur University Department of CSE Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. Our method uses a multi-modal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, the proposed framework can work for both offline and online script identification which alleviates the need for designing two separate script identification modules for individual modality. We also propose a novel conditional multi-modal fusion scheme to combine the information from offline and online modality which takes into account the original modality of the data being fed to our network and thus it combines adaptively. An exhaustive experimental study has been done on a data set including English(Roman) and 6 other official Indic scripts. Our proposed scheme outperforms traditional classifiers along with handcrafted features and deep learning based methods. Experiment results show that using only character level training data can achieve competitive performance against traditional training using word level data. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

An end-to-end system for bangla online handwriting recognition 15

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An end-to-end system for bangla online handwriting recogniti...

15th International Conference on Frontiers in Handwriting recognition, ICFHR 2016

作者： Bhattacharya, Soumik Maitra, Durjoy Sen Bhattacharya, Ujjwal Parui, Swapan K. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781509009817

A few studies of online Bangla handwriting recognition such as isolated character recognition or limited vocabulary cursive word recognition are found in the literature. However, development of an end-to-end recognition system of unconstrained online Bangla handwritten texts has not been duly attempted so far. In the present report, we describe a similar system which takes a piece of continuous online handwritten Bangla texts as the input. It first segments the input texts into individual lines, each line into its constituent words and each word into sub-strokes. In the present study, 152 different symbols which include basic characters, character modifiers, frequently used conjunct characters, a few special characters and numerals have been considered. The entire set of sub-strokes obtained from the training sample set has been exhaustively studied by 3 experts and 76 different shapes of sub-strokes have been identified based on consensus among these experts. Also, it has been observed that a character may produce at most 3 sub-strokes. Since a piece of Bangla texts often contains either Bangla or English numerals, the present character set consists of both the numeral set and 3 numeral shapes are common to both the scripts. The proposed recognition system uses two classifiers, one for characters and the other for sub-strokes. Sub-strokes are fed to the character classifier in their temporal order. A single sub-stroke followed by two consecutive sub-strokes and finally three successive sub-strokes are passed to the character classifier and the first two top responses of the character classifier among the three cases are compared. If the difference is less than a threshold, the response of sub-stroke classifier is used to reach a final decision. The proposed system provided 94.3% character level accuracy on a test set consisting of 33,453 word samples written by 31 writers. © 2016 IEEE.

关键词： Character recognition

Author Correction: Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

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NPJ digital medicine 2022年第1期5卷 38页

作者： Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S C Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M Zangwill Alejandro F Frangi Tin Aung Ching-Yu Cheng Yu Qiao Xiulan Zhang Daniel S W Ting State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangzhou People's Republic of China. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sciences Shenzhen People's Republic of China. University of Chinese Academy of Sciences Beijing People's Republic of China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. gxtykyy@***. Handan City Eye Hospital Handan People's Republic of China. C-MER (Shenzhen) Dennis Lam Eye Hospital International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen) Shenzhen People's Republic of China. The Eye Hospital WMU at Hangzhou Hangzhou People's Republic of China. Department of Ophthalmology The Second Hospital of Jilin University Changchun People's Republic of China. SenseTime Group Limited Hong Kong People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Guizhou Medical University Kaili People's Republic of China. Department of Ophthalmology The Second Affiliated Hospital of Xi'an Jiaotong University Xi'an People's Republic of China. Department of Ophthalmology The Third Affiliated Hospital of Nanchang University Nanchang People's Republic of China. The First Hospital of Shijiazhuang City Shijiazhuang People's Republic of China. Hamilton Glaucoma Center Shiley Eye Institute Viterbi Family Department of Ophthalmology UC San Diego La Jolla CA United States. CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds UK. Singapore Eye Research Institute and Singapore National Eye Centre Singapore Singapore. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology The Chinese Academy of Sci