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2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael 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)9781479920723

In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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Automated industrial quality control of pipe stacks using computer vision 4th

Automated industrial quality control of pipe stacks using co...

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4th International Conference on computer vision and Image Processing, CVIP 2019

作者： Chatterjee, Sayantan Chaudhuri, Bidyut B. Nandi, Gora C. Department of Robotics and Artificial Intelligence Indian Institute of Information Technology Allahabad AllahabadUttar Pradesh211012 India Department of Computer Vision and Pattern Recognition Indian Statistical Institute Kolkata KolkataWest Bengal700108 India

ISBN: (纸本)9789811540141

In this work, we describe an automated quality assurance system for pipes in warehouses and yards using simple handheld and mobile equipment like smartphone cameras. Currently, quality inspection for bent and crooked pipe ends is done manually, which entails additional labour costs and is relatively slower than a mechanised approach. We propose an efficient and robust method of detecting the perfectly circular cross-sections of the pipes in stacks using an adaptive variation of Hough Transform algorithm. As a multistage approach, our proposed method first intensifies the foreground features relative to the background and then applies Canny edge detection to obtain the gradient details. The gradient directions are in turn fed to the modified Hough Transform algorithm using Hough gradient to detect the cross-sections. The novel Hough Transform modification features a region-based processing in a "coarse to fine" manner by dividing the image into smaller grids and detecting circular cross-sections per grid, which has computational advantages of using a smaller accumulator and is less memory intensive. Experiments were performed on real industrial images to validate the efficiency of the proposed algorithm and the results show that the proposed method can successfully and accurately highlight the perfectly circular cross-sections while leaving the faulty pipe-ends undetected. © Springer Nature Singapore Pte Ltd 2020.

关键词： Hough transforms

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Graphically Residual Attentive Network for tackling aerial image occlusion

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computers and Electrical Engineering 2025年 125卷

作者： Praveen Kumar Pradhan Kunal Purkayastha Aaditya Lochan Sharma Udayan Baruah Biswaraj Sen Palash Ghosal Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Rangpo 737136 Sikkim India Centre for Computers and Communication Technology Chisopani South Sikkim 737136 Sikkim India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata 700108 West Bengal India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Majitar Rangpo 737136 Sikkim India Birangana Sati Sadhani Rajyik Vishwavidyalaya Golaghat 785621 Assam India

Deep learning has rapidly advanced, enabling new applications such as object detection, text recognition, and occlusion handling. However, challenges remain in the detection of objects in complex environments such as aerial images where things like motion blur, low light, and significant occlusion occur. This paper addresses a similar challenge by introducing a novel supervised framework, the Graphically Residual Attentive Network (GRESIDAN). In the same model, GRESIDAN integrates three synergistic pipelines for object detection, occlusion detection, and occlusion removal. GRESIDAN uses a residually attentive block combining ResNet-18 and a multi-headed attention mechanism to improve feature extraction and detection accuracy in low-quality, occluded aerial images. A graphically attentive occlusion detection pipeline is implemented to handle occlusion, segment better, and mask out the occluder in the aerial image. The GRESIDAN model is validated on the COCO-2017 dataset and a custom private aerial object detection dataset, outperforming the state-of-the-art methods in handling occlusion and detecting objects. Our contributions provide a robust solution to the problem of detecting and handling occluded objects in aerial imagery, pushing the boundaries of automated visual recognition in challenging real-world scenarios. The code for public use in training and testing is available on GitHub .

关键词： Image segmentation

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3-D human motion estimation using regularization with 2-D feature point tracking

3-D human motion estimation using regularization with 2-D fe...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ya-Ming Wang Li Cao Wen-Qing Huang Research Center for Computer Vision and Pattern Recognition Zhejiang Institute of Science and Technology Hangzhou Zhejiang China

ISBN: (纸本)0780378652

A novel approach is proposed to 3-D human motion estimation using regularization. First, a method of feature point tracking is developed based on /spl alpha/-/spl beta/ filter and genetic algorithm. The outliers and occluded points can be solved by this method. Then, in order to deal with the ill-posed estimation problem, a regularization approach is proposed, which is based on the results of 2-D feature point tracking and the motion smoothness between consecutive estimation groups. Thus, the ill-posed problem is converted to a well-posed one. Experimental results also demonstrate the feasibility of the proposed approach.

关键词： Humans Motion estimation Tracking Filters Genetic algorithms computer vision Image sequences Biological system modeling Equations pattern recognition

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MIO: Mutual Information Optimization using Self-Supervised Binary Contrastive Learning

arXiv

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arXiv 2021年

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

Self-supervised contrastive learning frameworks have progressed rapidly over the last few years. In this paper, we propose a novel mutual information optimization-based loss function for contrastive learning. We model our pre-training task as a binary classification problem to induce an implicit contrastive effect and predict whether a pair is positive or negative. We further improve the näive loss function using the Majorize-Minimizer principle and such improvement helps us to track the problem mathematically. Unlike the existing methods, the proposed loss function optimizes the mutual information in both positive and negative pairs. We also present a closed-form expression for the parameter gradient flow and compare the behavior of the proposed loss function using its Hessian eigen-spectrum to analytically study the convergence of SSL frameworks. The proposed method outperforms the SOTA contrastive self-supervised frameworks on benchmark datasets like CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet. After 200 epochs of pre-training with ResNet-18 as the backbone, the proposed model achieves an accuracy of 86.2%, 58.18%, 77.49%, and 30.87% on CIFAR-10, CIFAR-100, STL-10, and Tiny-ImageNet datasets, respectively, and surpasses the SOTA contrastive baseline by 1.23%, 3.57%, 2.00%, and 0.33%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Segmentation of coarticulated basic units in isolated Bangla words using multifactorial analysis

Segmentation of coarticulated basic units in isolated Bangla...

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IEEE India Conference (INDICON)

作者： T. Chattopadhyay B.B. Chaudhuri Systems TCS Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

One of the major challenges in speech synthesis and recognition is coarticulated unit segmentation. In this paper we present a novel technique for segmenting the basic coarticulated units using multifactorial analysis based approach. The proposed algorithm is applied on isolated spoken words in Bangla. The results obtained from a considerably large database show the strength of the approach.

关键词： Speech synthesis State-space methods Databases Automatic speech recognition Image segmentation Image processing Linear predictive coding Switches Testing Signal analysis

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recognition of handprinted bangla numerals using neural network models 5th

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5th International Conference on Asian Fuzzy Systems Society, AFSS 2002

作者： Bhattacharya, U. Das, T.K. Datta, A. Parui, S.K. Chaudhuri, B.B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Calcutta35 India Computer and Statistical Service Center Indian Statistical Institute 203 B. T. Road Calcutta35 India

ISBN: (纸本)9783540431503

This paper proposes an automatic recognition scheme for hand printed Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Neural network models

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Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Ensemble and Personalized Transformer Models for Subject Ide...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salvatore Calcagno Raffaele Mineo Daniela Giordano Concetto Spampinato Department of Electrical Electronics and Computer Engineering Pattern Recognition and Computer Vision Laboratory (PeRCeiVe Lab) University of Catania Italy

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2 nd on the subject identification task (with an accuracy of 93.85%) and 1 st on the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://***/perceivelab/e-prevention-icassp-2023.

关键词： Performance evaluation Codes Wearable computers Signal processing Transformers Acoustics Object recognition

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Preliminary Level Cardiac Abnormality Detection Using Wireless Telecardiology System

Preliminary Level Cardiac Abnormality Detection Using Wirele...

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International Conference on the Digital Society

作者： M. Mitra S. Mitra J.N. Bera R. Gupta B.B. Chaudhuri Department of Applied Physics University of Calcutta India Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781424431342;0769527604

This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks; the first one is required to be carried to the patient home, named 'Portable Telecardiology Kit'. The second one, named 'Automated Cardiac Signal Processor', a PC based system, to be permanently placed at the nearest rural health care center. The 'Portable Telecardiology Kit' contains a portable ECG machine, a dedicated microcontroller-based full duplex communication system, both interfaced, in a single enclosure. This kit will convert the ECG signal along with a tag page (containing different information regarding patient's health and history of illness) to a digitized serial electromagnetic wave. The signal may be transmitted over a distance of about 7-10 km through a cordless phone under full duplex mode. The PC-based system, after receiving the transmitted signal from patient site, extract the ECG signal after proper filtering, and then pass the signal through an in-built knowledge-base. Finally, a rule-based rough set decision system is generated for the development of an inference engine for disease identification from these time-plane features. The system will generate a report indicating the preliminary level abnormalities, and the precautions that can be adopted at an early stage. The entire report is then sent to the Portable kit at the patient bed-site.

关键词： Electrocardiography Telemedicine Medical services Artificial neural networks Set theory pattern recognition Fuzzy neural networks Diseases Discrete wavelet transforms Radiofrequency amplifiers

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An approach for Bangla and Devanagari video text recognition 13

An approach for Bangla and Devanagari video text recognition

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4th International Workshop on Multilingual OCR, MOCR 2013

作者： Banerjee, Purnendu Chaudhuri, B.B. Society for Natural Language Technology Research Module 130 SDF Building Kolkata-700091 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata- 108 India

ISBN: (纸本)9781450321143

Extraction and recognition of Bangla text from video frame images is challenging due to fonts type and style variation, complex color background, low-resolution, low contrast etc. In this paper, we propose an algorithm for extraction and recognition of Bangla and Devanagari text form video frames with complex background. Here, a two-step approach has been proposed. After text localization, the text line is segmented into words using information based on line contours. First order gradient values of the text blocks are used to find the word gap. Next, an Adaptive SIS binarization technique is applied on each word. Next this binarized text block is sent to a state of the art OCR for recognition. © 2013 ACM.

关键词： Extraction

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