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Debotosh Bhattacharjee

Bio: Debotosh Bhattacharjee is an academic researcher from Jadavpur University. The author has contributed to research in topics: Facial recognition system & Face (geometry). The author has an hindex of 21, co-authored 298 publications receiving 2401 citations. Previous affiliations of Debotosh Bhattacharjee include University of Hradec Králové & MCKV Institute of Engineering.


Papers
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Journal ArticleDOI
Neeraj Kumar1, Ruchika Verma2, Deepak Anand3, Yanning Zhou4, Omer Fahri Onder, E. D. Tsougenis, Hao Chen, Pheng-Ann Heng4, Jiahui Li5, Zhiqiang Hu6, Yunzhi Wang7, Navid Alemi Koohbanani8, Mostafa Jahanifar8, Neda Zamani Tajeddin8, Ali Gooya8, Nasir M. Rajpoot8, Xuhua Ren9, Sihang Zhou10, Qian Wang9, Dinggang Shen10, Cheng-Kun Yang, Chi-Hung Weng, Wei-Hsiang Yu, Chao-Yuan Yeh, Shuang Yang11, Shuoyu Xu12, Pak-Hei Yeung13, Peng Sun12, Amirreza Mahbod14, Gerald Schaefer15, Isabella Ellinger14, Rupert Ecker, Örjan Smedby16, Chunliang Wang16, Benjamin Chidester17, That-Vinh Ton18, Minh-Triet Tran19, Jian Ma17, Minh N. Do18, Simon Graham8, Quoc Dang Vu20, Jin Tae Kwak20, Akshaykumar Gunda21, Raviteja Chunduri3, Corey Hu22, Xiaoyang Zhou23, Dariush Lotfi24, Reza Safdari24, Antanas Kascenas, Alison O'Neil, Dennis Eschweiler25, Johannes Stegmaier25, Yanping Cui26, Baocai Yin, Kailin Chen, Xinmei Tian26, Philipp Gruening27, Erhardt Barth27, Elad Arbel28, Itay Remer28, Amir Ben-Dor28, Ekaterina Sirazitdinova, Matthias Kohl, Stefan Braunewell, Yuexiang Li29, Xinpeng Xie29, Linlin Shen29, Jun Ma30, Krishanu Das Baksi31, Mohammad Azam Khan32, Jaegul Choo32, Adrián Colomer33, Valery Naranjo33, Linmin Pei34, Khan M. Iftekharuddin34, Kaushiki Roy35, Debotosh Bhattacharjee35, Anibal Pedraza36, Maria Gloria Bueno36, Sabarinathan Devanathan37, Saravanan Radhakrishnan37, Praveen Koduganty37, Zihan Wu38, Guanyu Cai39, Xiaojie Liu39, Yuqin Wang39, Amit Sethi3 
TL;DR: Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.
Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

251 citations

Journal ArticleDOI
TL;DR: This work proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images using ICIAR 2018 breast histology image dataset which comprises of 4 different classes namely normal, benign, in situ and invasive cancer.

144 citations

Posted Content
TL;DR: The characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten DevNagari characters are discussed.
Abstract: Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features These features are then fed to Neural classifier and in support vector machine for classification In neural classifier, we explored three ways of combining decisions of four MLP’s, designed for four different features

113 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: In this paper, the authors used four feature extraction techniques namely, intersection, shadow feature, chain code histogram, and straight line fitting features for handwritten Devnagari characters recognition using weighted majority voting technique.
Abstract: In this paper, we present an OCR for handwritten Devnagari characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four multi layer perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for handwritten Devnagari character recognition and it has been observed that this approach has better success rate than other methods.

110 citations

Book ChapterDOI
27 Jul 2011
TL;DR: The role of different IR spectrums, their applications, some interesting critical observations, available thermal databases, review works, some experimental results on thermal faces as well as on fused faces of visual and thermal face images in face recognition field; and finally sorting their limitations out are introduced.
Abstract: Face of an individual is a biometric trait that can be used in computer-based automatic security system for identification or authentication of that individual. While recognizing a face through a machine, the main challenge is to accurately match the input human face with the face image of the same person already stored in the face-database of the system. Not only the computer scientists, but the neuroscientists and psychologists are also taking their interests in the field of development and improvement of face recognition. Numerous applications of it relate mainly to the field of security. Having so many applications of this interesting area, there are challenges as well as pros and cons of the systems. Face image of a subject is the basic input of any face recognition system. Face images may be of different types like visual, thermal, sketch and fused images. A face recognition system suffers from some typical problems. Say for example, visual images result in poor performance with illumination variations, such as indoor and outdoor lighting conditions, low lighting, poses, aging, disguise etc. So, the main aim is to tackle all these problems to give an accurate automatic face recognition. These problems can be solved using thermal images and also using fused images of visual and thermal images. The image produced by employing fusion method provides the combined information of both the visual and thermal images and thus provides more detailed and reliable information which helps in constructing more efficient face recognition system. Objective of this chapter is to introduce the role of different IR spectrums, their applications, some interesting critical observations, available thermal databases, review works, some experimental results on thermal faces as well as on fused faces of visual and thermal face images in face recognition field; and finally sorting their limitations out.

74 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

21 Jan 2018
TL;DR: It is shown that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men, in commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition.
Abstract: The paper “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru, that will be presented at the Conference on Fairness, Accountability, and Transparency (FAT*) in February 2018, evaluates three commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. The study finds these services to have recognition capabilities that are not balanced over genders and skin tones [1]. In particular, the authors show that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men.

2,528 citations

Proceedings Article
01 Jan 1989
TL;DR: A scheme is developed for classifying the types of motion perceived by a humanlike robot and equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented.
Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

2,000 citations

01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

1,758 citations