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1,053 results on '"Bhattacharjee, Debotosh"'

1. Hand Biometrics in Digital Forensics

Author

Bera, Asish, Bhattacharjee, Debotosh, and Nasipuri, Mita

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Digital forensic is now an unavoidable part for securing the digital world from identity theft. Higher order of crimes, dealing with a massive database is really very challenging problem for any intelligent system. Biometric is a better solution to win over the problems encountered by digital forensics. Many biometric characteristics are playing their significant roles in forensics over the decades. The potential benefits and scope of hand based modes in forensics have been investigated with an illustration of hand geometry verifi-cation method. It can be applied when effective biometric evidences are properly unavailable; gloves are damaged, and dirt or any kind of liquid can minimize the accessibility and reliability of the fingerprint or palmprint. Due to the crisis of pure uniqueness of hand features for a very large database, it may be relevant for verification only. Some unimodal and multimodal hand based biometrics (e.g. hand geometry, palmprint and hand vein) with several feature extractions, database and verification methods have been discussed with 2D, 3D and infrared images.

Published
2024

2. Finger Biometric Recognition With Feature Selection

Author

Bera, Asish, Bhattacharjee, Debotosh, and Nasipuri, Mita

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Biometrics is indispensable in this modern digital era for secure automated human authentication in various fields of machine learning and pattern recognition. Hand geometry is a promising physiological biometric trait with ample deployed application areas for identity verification. Due to the intricate anatomic foundation of the thumb and substantial inter-finger posture variation, satisfactory performances cannot be achieved while the thumb is included in the contact-free environment. To overcome the hindrances associated with the thumb, four finger-based (excluding the thumb) biometric approaches have been devised. In this chapter, a four-finger based biometric method has been presented. Again, selection of salient features is essential to reduce the feature dimensionality by eliminating the insignificant features. Weights are assigned according to the discriminative efficiency of the features to emphasize on the essential features. Two different strategies namely, the global and local feature selection methods are adopted based on the adaptive forward-selection and backward-elimination (FoBa) algorithm. The identification performances are evaluated using the weighted k-nearest neighbor (wk-NN) and random forest (RF) classifiers. The experiments are conducted using the selected feature subsets over the 300 subjects of the Bosphorus hand database. The best identification accuracy of 98.67%, and equal error rate (EER) of 4.6% have been achieved using the subset of 25 features which are selected by the rank-based local FoBa algorithm., Comment: 34 pages. The Biometric Computing: Recognition and Registration, 2019

Published
2023

4. Deep Neural Networks Fused with Textures for Image Classification

Author

Bera, Asish, Bhattacharjee, Debotosh, and Nasipuri, Mita

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modelling using the long short-term memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins., Comment: 14 pages, 6 figures, 4 tables, conference

Published
2023
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5. Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

Author

Bera, Asish, Nasipuri, Mita, Krejcar, Ondrej, and Bhattacharjee, Debotosh

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net's accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://sites.google.com/view/syd-net/home., Comment: 12 pages, 12 figures, 10 tables

Published
2023
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6. Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

Author

Jha, Debesh, Sharma, Vanshali, Banik, Debapriya, Bhattacharya, Debayan, Roy, Kaushiki, Hicks, Steven A., Tomar, Nikhil Kumar, Thambawita, Vajira, Krenzer, Adrian, Ji, Ge-Peng, Poudel, Sahadev, Batchkala, George, Alam, Saruar, Ahmed, Awadelrahman M. A., Trinh, Quoc-Huy, Khan, Zeshan, Nguyen, Tien-Phat, Shrestha, Shruti, Nathan, Sabari, Gwak, Jeonghwan, Jha, Ritika K., Zhang, Zheyuan, Schlaefer, Alexander, Bhattacharjee, Debotosh, Bhuyan, M. K., Das, Pradip K., Fan, Deng-Ping, Parsa, Sravanthi, Ali, Sharib, Riegler, Michael A., Halvorsen, Pål, De Lange, Thomas, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems.

Published
2023

7. WSports-50: An Image Dataset for Women’s Sport Action Classification

Author

Ray, Palash, Bera, Asish, Krejcar, Ondrej, Bhattacharjee, Debotosh, Nasipuri, Mita, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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8. COMSYS Hackathon-1 2023: Igniting Machine Learning Marvels

Author

Paul, Aryan, Mittal, Om, Ghosh, Sounak, Dasgupta, Sushmit, Bhattacharjee, Debotosh, Sarkar, Ram, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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9. CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-19 Detection in CT-Scans

Author

Das, Ankit, Banik, Debapriya, Roy, Kaushiki, Chan, Gordon K., Bhattacharjee, Debotosh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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10. SSANet: Side-by-Side Additive Network for Knee Osteoarthritis Severity Detection from X-Ray Images

Author

Tewari, Rakhi, Bhattacharjee, Debotosh, Roy, Hiranmoy, Krejcar, Ondrej, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kole, Dipak Kumar, editor, Roy Chowdhury, Shubhajit, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2024
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16. A Survey on COVID-19 Lesion Segmentation Techniques from Chest CT Images

Author

Roy, Kaushiki, Bhattacharjee, Debotosh, Krejcar, Ondrej, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarkar, Ram, editor, Pal, Sujata, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2023
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17. Deep Learning for Segmentation of Polyps for Early Prediction of Colorectal Cancer: A Prosperous Direction

Author

Banik, Debapriya, Krejcar, Ondrej, Bhattacharjee, Debotosh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarkar, Ram, editor, Pal, Sujata, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2023
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18. No-Reference Image Quality Assessment Using Meta-Learning

Author

Dey, Ratnadeep, Bhattacharjee, Debotosh, Kejcar, Ondrej, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sarkar, Ram, editor, Pal, Sujata, editor, Basu, Subhadip, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2023
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19. Classification of Breast Tumor from Ultrasound Images Using No-Reference Image Quality Assessment

Author

Dey, Ratnadeep, Bhattacharjee, Debotosh, Kollmann, Christian, Krejcar, Ondrej, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Basu, Subhadip, editor, Kole, Dipak Kumar, editor, Maji, Arnab Kumar, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2023
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20. BUS-Net: A Fusion-based Lesion Segmentation Model for Breast Ultrasound (BUS) Images

Author

Roy, Kaushiki, Bhattacharjee, Debotosh, Kollmann, Christian, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Basu, Subhadip, editor, Kole, Dipak Kumar, editor, Maji, Arnab Kumar, editor, Plewczynski, Dariusz, editor, and Bhattacharjee, Debotosh, editor

Published
2023
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23. Spoofing Detection on Hand Images Using Quality Assessment

Author

Bera, Asish, Dey, Ratnadeep, Bhattacharjee, Debotosh, Nasipuri, Mita, and Shum, Hubert P. H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification er ror using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples.

Published
2021
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26. Quasistatic Computation and Thermodynamic Approach

Author

Bhattacharjee, Ranadeep, Bhattacharjee, Debotosh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Chaki, Rituparna, editor, Chaki, Nabendu, editor, Cortesi, Agostino, editor, and Saeed, Khalid, editor

Published
2023
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27. SARM-Net: A Spatial Attention-Based Residual M-Net for Polyp Segmentation

Author

Banik, Debapriya, Bhattacharjee, Debotosh, Tavares, João Manuel R. S., Series Editor, Jorge, Renato Natal, Series Editor, Frangi, Alejandro, Editorial Board Member, BAJAJ, CHANDRAJIT, Editorial Board Member, Onate, Eugenio, Editorial Board Member, Perales, Francisco José, Editorial Board Member, Holzapfel, Gerhard A., Editorial Board Member, Vilas-Boas, João, Editorial Board Member, Weiss, Jeffrey, Editorial Board Member, Middleton, John, Editorial Board Member, Garcia Aznar, Jose Manuel, Editorial Board Member, Nithiarasu, Perumal, Editorial Board Member, Tamma, Kumar K., Editorial Board Member, Cohen, Laurent, Editorial Board Member, Doblare, Manuel, Editorial Board Member, Prendergast, Patrick J., Editorial Board Member, Löhner, Rainald, Editorial Board Member, Kamm, Roger, Editorial Board Member, Li, Shuo, Editorial Board Member, Hughes, Thomas J.R., Editorial Board Member, Zhang, Yongjie, Editorial Board Member, Gupta, Mousumi, editor, Ghatak, Sujata, editor, Gupta, Amlan, editor, and Mukherjee, Abir Lal, editor

Published
2023
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28. Domain-Specific Cues for the Usability of Marker-Controlled Watershed Algorithm and U-Net for Medical Image Segmentation

Author

Roy, Kaushiki, Bhattacharjee, Debotosh, Khatun, Masuma, Dutta, Anindita, Tavares, João Manuel R. S., Series Editor, Jorge, Renato Natal, Series Editor, Frangi, Alejandro, Editorial Board Member, BAJAJ, CHANDRAJIT, Editorial Board Member, Onate, Eugenio, Editorial Board Member, Perales, Francisco José, Editorial Board Member, Holzapfel, Gerhard A., Editorial Board Member, Vilas-Boas, João, Editorial Board Member, Weiss, Jeffrey, Editorial Board Member, Middleton, John, Editorial Board Member, Garcia Aznar, Jose Manuel, Editorial Board Member, Nithiarasu, Perumal, Editorial Board Member, Tamma, Kumar K., Editorial Board Member, Cohen, Laurent, Editorial Board Member, Doblare, Manuel, Editorial Board Member, Prendergast, Patrick J., Editorial Board Member, Löhner, Rainald, Editorial Board Member, Kamm, Roger, Editorial Board Member, Li, Shuo, Editorial Board Member, Hughes, Thomas J.R., Editorial Board Member, Zhang, Yongjie, Editorial Board Member, Gupta, Mousumi, editor, Ghatak, Sujata, editor, Gupta, Amlan, editor, and Mukherjee, Abir Lal, editor

Published
2023
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31. List of contributors

Author

Abdelmaksoud, Islam R., primary, AbouElfetouh, Ahmed, additional, Abouelkheir, Rasha, additional, Alksas, Ahmed, additional, Andrearczyk, Vincent, additional, Bakas, Spyridon, additional, Bazoukis, George, additional, Benjelloun, Mohammed, additional, Bhattacharjee, Debotosh, additional, Brodzicki, Andrzej, additional, Chellappa, Rama, additional, Conant, Emily F., additional, Drisis, Stylianos, additional, El Adoui, Mohammed, additional, El-Baz, Ayman S., additional, El-Ghar, Mohamed Abou, additional, Elmahdy, Ahmed, additional, Elmogy, Mohammed, additional, Gastounioti, Aimilia, additional, Gehlot, Shiv, additional, Ghazal, Mohammed, additional, Giridharan, Guruprasad, additional, Gorgon, Marek, additional, Graziani, Mara, additional, Gupta, Anubha, additional, Gupta, Ritu, additional, Huang, Junzhou, additional, Jaworek-Korjakowska, Joanna, additional, Khaled, Reem, additional, Khalifa, Fahmi, additional, Kleczek, Pawel, additional, Kontos, Despina, additional, Larhmam, Mohammed Amine, additional, Lee, Sharen, additional, Li, Ruoyu, additional, Li, Xinmu, additional, Liao, Haofu, additional, Lin, Wei-An, additional, Liu, Tong, additional, Maghsoudi, Omid Haji, additional, Mahmoud, Ali, additional, Müller, Henning, additional, Naglah, Ahmed, additional, Nguyen, Truc, additional, Peng, Cheng, additional, Pernkopf, Franz, additional, Rathore, Saima, additional, Razek, Ahmed Abdel Khalek Abdel, additional, Shalaby, Ahmed, additional, Shehata, Mohamed, additional, Tse, Gary, additional, Yap, Moi Hoon, additional, and Zhou, Shaohua Kevin, additional

Published
2023
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40. Human Gender Classification Based on Hand Images Using Deep Learning

Author

Mukherjee, Rajesh, Bera, Asish, Bhattacharjee, Debotosh, Nasipuri, Mita, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sk, Arif Ahmed, editor, Turki, Turki, editor, Ghosh, Tarun Kumar, editor, Joardar, Subhankar, editor, and Barman, Subhabrata, editor

Published
2022
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42. Deep Learning Based Identification of Three Exotic Carps

Author

Banerjee, Arnab, Chakraborty, Roopsia, Behra, Samarendra, Srinivasan, Nagesh Talagunda, Bhattacharjee, Debotosh, Das, Nibaran, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Asit Kumar, editor, Nayak, Janmenjoy, editor, Naik, Bighnaraj, editor, Vimal, S., editor, and Pelusi, Danilo, editor

Published
2022
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47. A Novel Approach to 3D Face Registration for Biometric Analysis Using RCompute_ICP

Author

Bagchi, Parama, Bhattacharjee, Debotosh, Nasipuri, Mita, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharjee, Debotosh, editor, Kole, Dipak Kumar, editor, Dey, Nilanjan, editor, Basu, Subhadip, editor, and Plewczynski, Dariusz, editor

Published
2021
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