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arXiv 2022年

作者： Chattopadhyay, Soumitri Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Dept. of Information Technology Jadavpur University Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India

Offline Signature Verification (OSV) is a fundamental biometric task across various forensic, commercial and legal applications. The underlying task at hand is to carefully model fine-grained features of the signatures to distinguish between genuine and forged ones, which differ only in minute deformities. This makes OSV more challenging compared to other verification problems. In this work, we propose a two-stage deep learning framework that leverages self-supervised representation learning as well as metric learning for writer-independent OSV. First, we train an image reconstruction network using an encoder-decoder architecture that is augmented by a 2D spatial attention mechanism using signature image patches. Next, the trained encoder backbone is fine-tuned with a projector head using a supervised metric learning framework, whose objective is to optimize a novel dual triplet loss by sampling negative samples from both within the same writer class as well as from other writers. The intuition behind this is to ensure that a signature sample lies closer to its positive counterpart compared to negative samples from both intra-writer and cross-writer sets. This results in robust discriminative learning of the embedding space. To the best of our knowledge, this is the first work of using self-supervised learning frameworks for OSV. The proposed two-stage framework has been evaluated on two publicly available offline signature datasets and compared with various state-of-the-art methods. It is noted that the proposed method provided promising results outperforming several existing pieces of work. The code is publicly available at: https://***/soumitri2001/SURDS-SSL-OSV. © 2022, CC BY.

关键词： Image reconstruction

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ICFHR 2012 Competition on recognition of On-Line Mathematical Expressions (CROHME 2012)

ICFHR 2012 Competition on Recognition of On-Line Mathematica...

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International Workshop on Frontiers in Handwriting recognition

作者： Harold Mouchère Christian Viard-Gaudin D.H. Kim J.H. Kim U. Garain IRCCyN/IVC-UMR CNRS 6597 Ecole Polytechnique de l'Iniversité de Nantes France Division of Computer Science Korea Advanced Institute of Science and Technology South Korea Computer Vision and Pattern Recognition (CVPR) Unit Indian Statistical Institute India

This paper presents an overview of the second Competition on recognition of Online Handwritten Mathematical Expressions, CROHME 2012. The objective of the contest is to identify current advances in mathematical expression recognition using common evaluation performance measures and datasets. This paper describes the contest details including the evaluation measures used as well as the performance of the 7 submitted systems along with a short description of each system. Progress as compared to the 1st version of CROHME is also documented.

关键词： Handwriting recognition

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A colour image encryption based on DNA coding and chaotic sequences

A colour image encryption based on DNA coding and chaotic se...

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International Conference on Emerging Trends and Applications in computer Science (ICETACS)

作者： Sukalyan Som Atanu Kotal Ayantika Chatterjee Soumista Dey Sarbani Palit Department of Computer Science Barrackpore Rastraguru Surendranath College Kolkata West Bengal India Department of Computer Science and Engineering Techno India College of Technology Kolkata West Bengal India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata West Bengal India

In this communication, a Chaos Based Symmetric Key Encryption of RGB Color Images with DNA Coding and a Chaos based Pseudorandom Binary Number Generator (PRBNG) has been proposed. In the proposed algorithm, the plain image is first scrambled using generalized Arnold Cat Map to achieve confusion. The scrambled image pixel are converted to DNA codes and again reconverted to integers where the choice of DNA coding rule is made pseudorandom based on the binary sequences generated by chaos based pseudorandom binary number generator. The integers thus obtained are diffused by performing exclusive OR operation with the integer sequences generated by 1D Logistic map producing the cipher image. The experimental results depicts that the proposed algorithm can successfully encrypt and decrypt RGB color images with secret keys. The simulation analysis also exhibit that the proposed method is loss-less, secure and efficient measured in terms of statistical tests(like histogram analysis, correlation coefficient analysis, measures of central tendency and dispersion), key sensitivity test, key space analysis, information entropy test, encryption quality by MSE, PSNR, NPCR and UACI.

关键词： DNA Ciphers Encryption Chaotic communication Logistics

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran The Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India The AI Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as ‘attacks,’ underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, and CalTech256, focusing on image classification with 12 different NUI attack models. The resilience of VGG, ResNet, MobilenetV3-small and InceptionV3 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models’ classification accuracy when subjected to NUI attacks, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models’ resilience against NUI attacks. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document Image Binarization 23

TransDocUNet: A Transformer-based UNet Architecture for Degr...

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Proceedings of the Fourteenth Indian Conference on computer vision, Graphics and Image Processing

作者： Risab Biswas Soumik Sarkhel Swalpa Kumar Roy Umapada Pal Artificial Intelligence Group Optiks Innovations Pvt. Ltd. (P360) India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images.

关键词： Binarization CNN Deep Learning Document Image Enhancement TransUNet UNet vision Transformer

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LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications

arXiv

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arXiv 2024年

作者： Shah, Rahul Saha, Soumadeep Mukherjee, Purba Garain, Utpal Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India

We investigate the prospect of reconstructing the "cosmic distance ladder" of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernovae compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other datasets like baryon acoustic oscillations, calibration of high-redshift datasets such as gamma ray bursts, and use as a model-independent mock catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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Local jet pattern: A robust descriptor for texture classification

arXiv

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arXiv 2017年

作者： Roy, Swalpa Kumar Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Dubey, Shiv Ram Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India Department of Electronics & Communication Engineering National Institute of Technology Rourkela Orissa769008 India Computer Vision Group Indian Institute of Information Technology Sri City Andhra Pradesh517646 India

Methods based on locally encoded image features have recently become popular for texture classification tasks, particularly in the existence of large intra-class variation due to changes in illumination, scale, and viewpoint. Inspired by the theories of image structure analysis, this work proposes an efficient, simple, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is computed from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so-called local jet vectors (LJV), which also satisfy the Scale Space properties. The LJP is obtained by using the relation of center pixel with its' local neighborhoods in jet space. Finally, the feature vector of a texture image is formed by concatenating the histogram of LJP for all elements of LJV. All DtGs responses up to second order together preserves the intrinsic local image structure, and achieves invariance to scale, rotation, and reflection. This allows us to design a discriminative and robust framework for texture classification. Extensive experiments on five standard texture image databases, employing nearest subspace classifier (NSC), the proposed descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for Outex_TC10, Outex_TC12, KTH-TIPS, Brodatz, CUReT, respectively, which are better compared to state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Textures

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New Tampered Features for Scene and Caption Text Classification in Video Frame

New Tampered Features for Scene and Caption Text Classificat...

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International Workshop on Frontiers in Handwriting recognition

作者： Sangheeta Roy Palaiahnakote Shivakumara Umapada Pal Tong Lu Chew Lim Tan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science National University of Singapore

ISBN: (纸本)9781509009824

The presence of both caption/graphics/superimposed and scene texts in video frames is the major cause for the poor accuracy of text recognition methods. This paper proposes an approach for identifying tampered information by analyzing the spatial distribution of DCT coefficients in a new way for classifying caption and scene text. Since caption text is edited/superimposed, which results in artificially created texts comparing to scene texts that exist naturally in frames. We exploit this fact to identify the presence of caption and scene texts in video frames based on the advantage of DCT coefficients. The proposed method analyzes the distributions of both zero and non-zero coefficients (only positive values) locally by moving a window, and studies histogram operations over each input text line image. This generates line graphs for respective zero and non-zero coefficient coordinates. We further study the behavior of text lines, namely, linearity and smoothness based on centroid location analysis, and the principal axis direction of each text line for classification. Experimental results on standard datasets, namely, ICDAR 2013 video, 2015 video, YVT video and our own data, show that the performances of text recognition methods are improved significantly after-classification compared to before-classification.

关键词： Discrete cosine transforms Text recognition Image color analysis Linearity Bars Character recognition Mathematical model

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CROHME2011: Competition on recognition of Online Handwritten Mathematical Expressions

CROHME2011: Competition on Recognition of Online Handwritten...

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International Conference on Document Analysis and recognition

作者： Harold Mouchere Christian Viard-Gaudin Dae Hwan Kim Jin Hyung Kim Utpal Garain IRCCyN/IVC — UMR CNRS 6597 Ecole Polytechnique de l'Université de Nantes France Division of Computer Science Korea Advanced Institute of Science and Technology Republic of Korea Computer Vision and Pattern Recognition (CVPR) Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781457713507

A competition on recognition of online handwritten mathematical expressions is organized. recognition of mathematical expressions has been an attractive problem for the pattern recognition community because of the presence of enormous uncertainties and ambiguities as encountered during parsing of the two-dimensional structure of expressions. The goal of this competition is to bring out a state of the art for the related research. Three labs come together to organize the event and six other research groups participated the competition. The competition defines a standard format for presenting information, provides a training set of 921 expressions and supplies the underlying grammar for understanding the content of the training data. Participants were invited to submit their recognizers which were tested with a new set of 348 expressions. Systems are evaluated based on four different aspects of the recognition problem. However, the final rating of the systems is done based on their correct expression recognition accuracies. The best expression level recognition accuracy (on the test data) shown by the competing systems is 19.83% whereas a baseline system developed by one of the organizing groups reports an accuracy 22.41% on the same data set.

关键词： Grammar Handwriting recognition Training Accuracy Ink Organizing Communities

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Bag-of-visual-words for signature-based multi-script document retrieval

arXiv

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arXiv 2018年

作者： Mandal, Ranju Roy, Partha Pratim Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University QLD Australia Dept. of Computer Science & Engineering Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia

An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The 'Tobacco' [1] document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

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