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2011 International Conference on Image Information Processing, ICIIP 2011

作者： Pramanik, Sayak Mitra, Rupendra Nath Mitra, Sucharita Chaudhuri, Bidyut Baran Department of Electronics and Communication Institute of Engineering and Management Kolkata India Network Planning and Optimization Nokia Siemens Networks Pvt. Ltd. Kolkata India Department of Electronics Netaji Nagar Day College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781612848617

This paper reports the development of a software suite to be accessed in future with any General Packet Radio Service (GPRS) enabled mobile phone or Personal Digital Assistant (PDA) for the extraction and analysis of disease-related features from the images of paper based ECG records. In India and other developing countries, the cheaper paper based ECG machines are mainly used. Also in rural areas of these countries - where there is acute dearth of qualified cardiologists, the cardiac diseases are still the major silent killers. One way of addressing this problem is cellular medicine which necessitates an intelligent cardiac parameter extraction algorithm. In our bid to address exactly this requirement, a software is developed with the help of few image processing techniques. At first, the background noises i.e. the gridlines are removed by thresholding technique. Binarization and thinning has been done for extraction of digital time data and then Discrete Wavelet Transform (DWT) and water reservoir based pattern recognition technique are subsequently used to delineate other important ECG parameters for ECG interpretation. © 2011 IEEE.

关键词： Electrocardiograms

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Image-based characterization of the pulp flows

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pattern recognition and Image Analysis 2016年第3期26卷 630-637页

作者： Sorokin, M. Strokina, N. Eerola, T. Lensu, L. Karttunen, K. Kalviainen, H. Machine Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta University of Technology PO Box 20 LappeenrantaFI-53851 Finland Computer Vision Group Department of Signal Processing Tampere University of Technology PO Box 527 TampereFI-33101 Finland Cemis-Oulu Unit of Measurement Technology Kajaani University Consortium University of Oulu PO Box 127 KajaaniFI-87400 Finland School of Information Technology Monash University Malaysia Jalan Lagoon Selatan Bandar Sunway Selangor Darul Ehsan46150 Malaysia

Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions. © 2016, Pleiades Publishing, Ltd.

关键词： pattern matching

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Text/Graphics Separation in Color Maps

Text/Graphics Separation in Color Maps

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International Conference on Computing: Theory and Applications, ICCTA

作者： Partha Pratim Roy Josep Llados Umapada Pal Computer Science Department Universitat Autònoma de Barcelona Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India

Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper discusses a number of improvements to text/graphics separation methods to make it suitable for maps. Emphasize is given to the overlapping regions of text and graphics. It also discusses a method of color separation using clustering method for the purpose of text/graphics separation

关键词： computer graphics Image color analysis Image segmentation Histograms Bit error rate computer vision Text analysis Clustering methods Engineering drawings Testing

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AGA-GAN: Attribute guided attention generative adversarial network with U-net for face hallucination

arXiv

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arXiv 2021年

作者： Srivastava, Abhishek Chanda, Sukalpa Pal, Umapada Computer Vision and Pattern Recognition Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Communication Østfold University College Halden Norway

The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process on various facial features in the image. Stacking multiple AGA modules enables the recovery of both high and low-level facial structures. We design the discriminator to learn discriminative features exploiting the relationship between the high-resolution image and their corresponding facial attribute annotations. We then explore the use of U-Net based architecture to refine existing predictions and synthesize further facial details. Extensive experiments across several metrics show that our AGA-GAN and AGA-GAN+U-Net framework outperforms several other cutting-edge face hallucination state-of-the-art methods. We also demonstrate the viability of our method when every attribute descriptor is not known and thus, establishing its application in real-world scenarios. © 2021, CC BY.

关键词： Generative adversarial networks

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A multiple-classifier system for recognition of printed mathematical symbols

A multiple-classifier system for recognition of printed math...

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International Conference on pattern recognition

作者： U. Garain B.B. Chaudhuri R.P. Ghosh Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Student Department of Computer Science & Engineering MCKV Institute of Engineering Howrah West Bengal India

This paper deals with recognition of printed mathematical symbols. A group of classifiers arranged hierarchically is used to achieve robust recognition of the large number of symbols appearing in expressions. The classifier used at the top level employs stroke-based classification technique to recognize some of the frequently occurring symbols. The second level uses three classifiers to recognize the rest of the expression symbols. Different combination techniques have been attempted to integrate the second level classifiers to achieve high recognition accuracy. Experiment shows that the proposed approach is quite robust for recognition of a large number of symbols appearing in various expressions.

关键词： pattern recognition Robustness Feature extraction Shape computer vision computer science Handwriting recognition Testing Character recognition Typesetting

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Endafd: An End-to-End System for Ecg Based Atrial Fibrillation Detection

SSRN

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SSRN 2022年

作者： Basak, Shubham Das, Sudip Dasgupta, Subhrajyoti Bhattacharya, Ujjwal Narayan, Pradeep Computer Vision and Pattern Recognition Unit Indian Statistical Institute WB Kolkata India Rabindranath Tagore International Institute of Cardiac Sciences WB Kolkata India

Atrial fibrillation is one of the most common arrhythmias with significant clinical implications. When it comes to detecting and categorising different types of heart diseases, machine learning and deep learning techniques have shown great results. There is a tendency to use convolution-based neural networks, as they have been specifically designed to categorise and recognise images. Image classification entails extracting image attributes in order to identify certain patterns in the dataset. The use of a basic ANN for this classifications would be computationally costly due to the large number of trainable parameters. Traditional convolution-based detection approaches, on the other hand, occasionally provided skewed findings since they had to integrate all 12 ecg leads to acquire the result. They will occasionally overlook the findings of a single lead abnormality if no other leads are shown to be defective. However, in the instance of a transformer, they take a single lead value at a time and check to see if there is any irregularity within that lead. Although the training may not always be successful owing to the small number of input samples, the results are typically rigged in favor of the negative since the number of negative samples is considerably larger than the positive ones We need a way to generate a larger number of balanced samples via the model, and then train the model to evaluate the original input. This is where the model comes in. A unique end-to-end model is suggested, consisting of two vision transformers connected by a generative adversarial network. © 2022, The Authors. All rights reserved.

关键词： Diseases

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Convex hull based approach for multi-oriented character recognition from graphical documents

Convex hull based approach for multi-oriented character reco...

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International Conference on pattern recognition

作者： Partha Pratim Roy Umapada Pal Josep Llados Fumitaka Kimura Computer Vision Center Universitat Autònoma De Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Mie Japan

ISBN: (纸本)9781424421749

In this paper, we present a scheme towards recognition of English character in multi-scale and multi-oriented environments. Graphical document such as map consists of text lines which appear in different orientation. Sometimes, characters in a single word may follow a curvilinear way to annotate the graphical curve lines. For recognition of such multi-scale and multi-oriented characters a Support Vector Machine (SVM) based scheme is presented in this paper. The feature used here is invariant to character orientation. Circular ring and convex hull have been used along with angular information of the contour pixels of the character to make the feature rotation invariant. We tested our proposed scheme on two different datasets. Combining circular and convex hull feature we have obtained 96.73% and 99.56% accuracy in these two datasets.

关键词： Character recognition pattern recognition Support vector machines Frequency Text recognition Histograms Clocks Rivers Optical character recognition software Testing

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Combination of product graph and random walk kernel for symbol spotting in graphical documents

Combination of product graph and random walk kernel for symb...

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International Conference on pattern recognition

作者： Anjan Dutta Jaume Gibert Josep Lladós Horst Bunke Umapada Pal Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Institute of Computer Science and Applied Mathematics Universitat Bern Bern Switzerland Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781467322164

This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.

关键词： Kernel Computational modeling pattern recognition Databases Labeling Equations Performance evaluation

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Machine Authentication of Security Documents

Machine Authentication of Security Documents

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International Conference on Document Analysis and recognition

作者： Utpal Garain Biswajit Halder Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of IT Greater Kolkata College of Engineering and Management Baruipur West Bengal India

ISBN: (纸本)9781424445004

This paper presents a pioneering effort towards machine authentication of security documents like bank cheques, legal deeds, certificates, etc. that fall under the same class as far as security is concerned. The proposed method first computationally extracts the security features from the document images and then the notion of dasiagenuinepsila vs. dasiaduplicatepsila is defined in the feature space. Bank cheques are taken as a reference for conducting the present experiment. Support Vector Machines (SVMs) and neural networks (NN) are involved to verify authenticity of these cheques. Results on a test dataset of 200 samples show that the proposed approach achieves about 98% accuracy for discriminating duplicate cheques from genuine ones. This strongly attests the viability of involving machine in authenticating security documents.

关键词： Authentication Security Printing Ink pattern recognition Feature extraction Support vector machines Neural networks Law Legal factors

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Color Feature Based Approach for Determining Ink Age in Printed Documents

Color Feature Based Approach for Determining Ink Age in Prin...

引用

International Conference on pattern recognition

作者： Biswajit Halder Utpal Garain Department of Information Technology Mallabhum Institute of Technology Bisnupur West Bengal India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE magazines published in between 1930's and 70's (five decades). Test results show that a viable framework for involving machines in assisting human experts for determining age of printed documents.

关键词： Ink Image color analysis Feature extraction Pixel Forensics Artificial neural networks Accuracy

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