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Annual IEEE Symposium on computer-Based Medical Systems

作者： Dipayan Das K.C. Santosh Umapada Pal Dept. of Electronics & Comm. Engg. National Institute of Technology Durgapur India Dept. of Computer Science University of South Dakota Vermillion SD USA Computer Vision & Pattern Rec. Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9781728194295

ISBN: (纸本)9781728194301

Automated radiological screening is an advancing field in which algorithms and predictive models are used to detect abnormalities in Chest X-rays (CXRs). Traditionally, in machine learning, the exact same dataset has been partitioned into train and test sets, and as a consequence, the validation scores are often biased towards the population it has been trained on. Cross-population test is a measure of how good an algorithm/model performs after training on a data from one region of the world and then evaluating the model on another data from another part of the world, without any additional training or learning on the latter data. To showcase cross-population train/test model, we consider two benchmark CXR (with Tuberculosis) datasets that are made available by the U.S. National Library of Medicine: a) Shenzhen, China; and b) Montgomery County, USA. We used a modified pre-trained deep learning model as our predictive model and achieved a cross-population classification accuracy of 76.05% (0.84, AUC) and 71.47% (0.79, AUC), using each dataset as training and testing data separately. To the best of our knowledge, this is the first cross-population evaluation of a deep learning model being used for abnormality screening using CXRs.

关键词： Machine learning Data models Solid modeling Training Feature extraction Sociology Statistics

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

arXiv

引用

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

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New COLD Feature Based Handwriting Analysis for Enthnicity/Nationality Identification

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

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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

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AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

arXiv

引用

arXiv 2021年

作者： Roy, Swalpa Kumar Paoletti, Mercedes E. Haut, Juan M. Dubey, Shiv Ram Kar, Purbayan Plaza, Antonio Chaudhuri, Bidyut B. The Computer Science and Engineering Alipurduar Government Engineering and Management College 736206 India The Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Cáceres10003 Spain The Computer Vision and Biometrics Lab Indian Institute of Information Technology Prayagraj Uttar Pradesh Allahabad211015 India The Media Analysis Group Sony Research India Private Limited Karnataka Bangalore560103 India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very popular due to its adaptive momentum, which tackles the dying gradient problem of SGD. Nevertheless, existing optimizers are still unable to exploit the optimization curvature information efficiently. This paper proposes a new AngularGrad optimizer that considers the behavior of the direction/angle of consecutive gradients. This is the first attempt in the literature to exploit the gradient angular information apart from its magnitude. The proposed AngularGrad generates a score to control the step size based on the gradient angular information of previous iterations. Thus, the optimization steps become smoother as a more accurate step size of immediate past gradients is captured through the angular information. Two variants of AngularGrad are developed based on the use of Tangent or Cosine functions for computing the gradient angular information. Theoretically, AngularGrad exhibits the same regret bound as Adam for convergence purposes. Nevertheless, extensive experiments conducted on benchmark data sets against state-of-the-art methods reveal a superior performance of AngularGrad. Source code: https://***/mhaut/AngularGrad. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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Local Gradient Difference Features for Classification of 2D-3D Natural Scene Text Images

Local Gradient Difference Features for Classification of 2D-...

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International Conference on pattern recognition

作者： Lokesh Nandanwar Palaiahnakote Shivakumara Ramachandra Raghavendra Tong Lu Umapada Pal Daniel Lopresti Nor Badrul Anuar Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Computer Science & Engineering Lehigh University Bethlehem PA USA

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

关键词： Three-dimensional displays Image recognition Graphical models Text recognition Artificial neural networks Feature extraction Standards

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Erratum to “Identification of different scripts lines from multi-script documents” [Image and vision Computing 20 (2002) 945–954]

引用

Image and vision Computing 2003年第11期21卷 1017-1017页

作者： U. Pal B.B. Chaudhuri Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Calcutta 700 035 India

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CRNN Based Jersey-Bib Number/Text recognition in Sports and Marathon Images

CRNN Based Jersey-Bib Number/Text Recognition in Sports and ...

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International Conference on Document Analysis and recognition

作者： Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

关键词： Text recognition Sports Image recognition Hip Thigh Torso Biological system modeling

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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and recognition — RRC-MLT-2019

ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Te...

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International Conference on Document Analysis and recognition

作者： Nibal Nayef Yash Patel Michal Busta Pinaki Nath Chowdhury Dimosthenis Karatzas Wafa Khlif Jiri Matas Umapada Pal Jean-Christophe Burie Cheng-lin Liu Jean-Marc Ogier no affiliation The Robotics Institute Carnegie Mellon Universiry Pittsburgh USA Department of Cybernetics Czech Technical University Prague Czech Republic CVPR unit Indian Statistical Institute India Computer Vision Center Universitat Autònoma de Barcelona Spain L3i Laboratory University of La Rochelle France National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

关键词： Task analysis Text recognition Training Benchmark testing Protocols Rendering (computer graphics)

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ICDAR2019 Robust reading challenge on multi-lingual scene text detection and recognition – RRC-MLT-2019

arXiv

引用

arXiv 2019年

作者： Nayef, Nibal Patel, Yash Busta, Michal Chowdhury, Pinaki Nath Karatzas, Dimosthenis Khlif, Wafa Matas, Jiri Pal, Umapada Burie, Jean-Christophe Liu, Cheng-lin Ogier, Jean-Marc L3i Laboratory University of La Rochelle France Computer Vision Center Universitat Autònoma de Barcelona Spain CVPR unit Indian Statistical Institute India Robotics Institute Carnegie Mellon Universiry Pittsburgh United States Center for Machine Perception Department of Cybernetics Czech Technical University Prague Czech Republic National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

关键词： Large dataset

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A new convolutional neural network based on a saprse convolutional layer for animal face detection

TechRxiv

引用

TechRxiv 2021年

作者： Jarraya, Islem BenSaid, Fatma Ouarda, Wael Pal, Umapada Alimi, Adel M. BP 1173 Sfax3038 Tunisia Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

This paper focuses on the face detection problem of three popular animal categories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actually investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the new network CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFDMobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively. © 2021, CC BY.

关键词： Animals

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