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TechRxiv 2022年

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada The Computer Vision and Patter Recognition Unit Indian Statistical Institute Kolkata India The Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India

In medical image analysis, the cost of acquiring high-quality data and their annotation by experts is a barrier in many medical applications. Most of the techniques used are based on supervised learning framework and need a large amount of annotated data to achieve satisfactory performance. As an alternative, in this paper, we propose a self-supervised learning (SSL) approach to learn the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful spatial context-invariant representations. The downstream task in our paper is a class imbalanced multi-label classification. Different experiments show that the features learnt by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy towards imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first work of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in class imbalanced multi-label classification tasks on MR videos. © 2022, CC BY.

关键词： Supervised learning

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ICFHR 2020 Competition on Short answer ASsessment and Thai student SIGnature and Name COMponents recognition and Verification (SASIGCOM 2020)

ICFHR 2020 Competition on Short answer ASsessment and Thai s...

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International Workshop on Frontiers in Handwriting recognition

作者： Abhijit Das Hemmaphan Suwanwiwat Umapada Pal Michael Blumenstein Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Information Technology Academy James Cook University Cairns Australia School of Software University of Technology Sydney Australia

ISBN: (数字)9781728199665

ISBN: (纸本)9781728199672

This paper describes the results of the competition on Short answer ASsessment and Thai student SIGnature and Name COMponents recognition and Verification (SASIGCOM 2020) in conjunction with the 17th International Conference on Frontiers in Handwriting recognition (ICFHR 2020). The competition was aimed to automate the evaluation process short answer-based examination and record the development and gain attention to such system. The proposed competition contains three elements which are short answer assessment (recognition and marking the answers to short-answer questions derived from examination papers), student name components (first and last names) and signature verification and recognition. Signatures and name components data were collected from 100 volunteers. For the Thai signature dataset, there are 30 genuine signatures, 12 skilled and 12 simple forgeries for each writer. With Thai name components dataset, there are 30 genuine and 12 skilfully forged name components for each writer. There are 104 exam papers in the short answer assessment dataset, 52 of which were written with cursive handwriting; the rest of 52 papers were written with printed handwriting. The exam papers contain ten questions, and the answers to the questions were designed to be a few words per question. Three teams from distinguished labs submitted their systems. For short answer assessment, word spotting task was also performed. This paper analysed the results produced by their algorithms using a performance measure and defines a way forward for this subject of research. Both the datasets, along with some of the accompanying ground truth/baseline mask will be made freely available for research purposes via the TC10/TC11.

关键词： Task analysis Handwriting recognition Training Particle measurements Atmospheric measurements Information technology Writing

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indian Multi-Script Full Pin-code String recognition for Postal Automation

Indian Multi-Script Full Pin-code String Recognition for Pos...

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International Conference on Document Analysis and recognition

作者： Umapada Pal Rami Kumar Roy Kaushik Roy Fumitaka Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computer Science West Bengal State University Barasat India Graduate School of Engineering TSU Mie University Japan

ISBN: (纸本)9781424445004

Under three-language formula, the destination address block of postal document of an indian state is generally written in three languages: English, Hindi and the State official language. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a pin-code is written. Also, because of the writing style of different individuals some of the digits in a pin-code string may touch with its neighboring digits. Accurate segmentation of such touching components into individual digits is a difficult task. To avoid such difficulties, in this paper we proposed a tri-lingual (English, Hindi and Bangla) 6-digit full pin-code string recognition. We obtained 99.01% reliability from our proposed system when error and rejection rates are 0.83% and 15.27%, respectively.

关键词： Automation Natural languages pattern recognition Writing Statistics Optical character recognition software Text analysis pattern analysis computer vision Databases

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Lexicon Reduction Technique for Bangla Handwritten Word recognition

Lexicon Reduction Technique for Bangla Handwritten Word Reco...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Tapan Kumar Bhowmik Utpal Roy Swapan K. Parui Faculty of Mathematics and Natural Sciences University of Groningam Netherlands Department of Computer and System Sciences Visva Bharati University Santiniketan India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also.

关键词： Handwriting recognition Hidden Markov models Shape Vectors Feature extraction Conferences

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Water reservoir based approach for touching numeral segmentation

Water reservoir based approach for touching numeral segmenta...

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International Conference on Document Analysis and recognition

作者： U. Pal A. Belaid C. Choisy Group READ LORIA Campus Scientifique France Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Group READ LORIA Campus Scientifique Vandoeuvre-les-Nancy France

ISBN: (纸本)0769512631

Deals with a scheme for automatic segmentation of unconstrained handwritten connected numerals. The scheme is mainly based on features obtained from a new concept based on a water reservoir. A reservoir is a metaphor to illustrate the region where numerals touch. The reservoir is obtained by considering accumulation of water poured from the top or from the bottom of the numerals. At first, considering the reservoir location and size, touching positions (top, middle and bottom) are decided. Next, by analyzing the reservoir boundary, touching position and topological features of the touching pattern, the best cutting point is determined. Finally, combined with morphological structural features the cutting path for segmentation is generated.

关键词： Water resources Reservoirs Water storage pattern analysis computer vision pattern recognition Handwriting recognition Joining IEEE

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A Kalman filtering induced heuristic optimization based partitional data clustering

arXiv

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arXiv 2019年

作者： Pakrashi, Arjun Chaudhuri, Bidyut B. Insight Centre for Data Analytics University College Dublin Ireland Computer Vision & Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space. In this paper at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K were compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Kalman filters

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SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

SSERBC 2017: Sclera segmentation and eye recognition benchma...

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2017 IEEE International Joint Conference on Biometrics, IJCB 2017

作者： Das, Abhijit Pal, Umapada Ferrer, Miguel A. Blumenstein, Michael Štepec, Dejan Rot, Peter Emeršič, Žiga Peer, Peter Štruc, Vitomir Kumar, S.V. Aruna Harish, B.S. 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 School of Software University of Technology Sydney Australia Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Information Science and Engineering Sri Jayachamrajendra College of Engineering Mysusru Karnataka India

ISBN: (纸本)9781538611241

This paper summarises the results of the Sclera Segmentation and Eye recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82×2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and peri-ocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject. © 2017 IEEE.

关键词： Competition

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A New U-Net Based License Plate Enhancement Model in Night and Day Images 5th

A New U-Net Based License Plate Enhancement Model in Night a...

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5th Asian Conference on pattern recognition, ACPR 2019

作者： Chowdhury, Pinaki Nath Shivakumara, Palaiahnakote Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Gjøvik Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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Tldsmi: Genetic Algorithm Based Network for Text Localization in Distorted Social Media Images

SSRN

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SSRN 2023年

作者： Palaiahnakote, Shivakumara Kumar, C. Pavan Aggarwal, Pranjal Sharma, Shubham Chandana, Pasupuleti Basavanna, M. Pal, Umapada Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Indian Institute of Information Technology Dharwad India University of Davanagere Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a novel model for understanding social image content through text localization. For text localization, we explore Maximally Stable Extremal Regions (MSER) for detecting components, that works by clustering pixels having similar properties. The output of component detection includes several non-text components due to degradations of social media images. To select the best components among many, we explore Genetic Algorithm by convolving different kernels with components, which results in a feature matrix which is further fed to EfficientNet for choosing actual text components. Therefore, the proposed model is called Genetic Algorithm based Network for Text Localization in Distorted Social Media Images (TLDSMI). For evaluating text localization, we consider the images of standard dataset of natural scene by uploading and downloading from different social media platforms, namely, WhatsApp, Telegram and Instagram. The effectiveness of our method is shown by testing on original and distorted standard datasets. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Music Instrument Identification Based on a 2-D Representation

Music Instrument Identification Based on a 2-D Representatio...

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International Conference on Electrical, Electronics, Communication, computer and Optimization Techniques (ICEECCOT)

作者： Alekhya Ghosh Arghadeep Pal Dibakar Sil Sarbani Palit Electronics and Communication Engineering IRPEL University of Calcutta Kolkata India Electronics and Communication Engineering National Institute of Technology Durgapur India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (数字)9781538651308

ISBN: (纸本)9781538651315

Automatic recognition of musical instruments has been a great problem in the field of digital signal processing. In this work a novel, compact set of features obtained by transforming a music signal to the spatial domain is introduced with promising results. Preliminary results on a popular music database, presented in terms of precision, recall and identification accuracy, are highly encouraging. The best result comprises an accuracy of 84.02% by Decision Tree (DT) for a set of 9 instruments belonging to different families. The accuracy for predicting the instrument family 96.07% for string family and for wind instrument the overall prediction accuracy is 90.78%. Further inclusion of some other features from some available works are also checked leading to slight increase in the accuracy. The obtained results are also compared with the result predicted by the VGG - 16 network on the same dataset. The feature set containing 8 features that introduces less computational complexity compared to available works, may be considered as a major contribution. The comparison shows, we can replicate nearly accurate classification through our proposed method in less amount of time than deep neural architecture.

关键词： Instruments Decision trees Forestry Feature extraction Timbre Matrix converters

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