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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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On foreground-background separation in low quality color document images

On foreground-background separation in low quality color doc...

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International Conference on Document Analysis and recognition

作者： Utpal Garain T. Paquet L. Heutte Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire PSI-FRE CNRS UFR des Sciences University of Rouen Mont Saint Aignan France

This paper proposes an adaptive method for separation of foreground and background in low quality color document images. A connected component labelling is initially implemented to capture the spatially connected similar color pixels. Next, dominant background components are determined to divide the entire image into number of grids each representing local uniformity in illumination, background, etc. Finally foreground parts are located using local information around them. Several color images of old historical documents including manuscripts of high importance are used in the experiment. Apart from a qualitative evaluation, results are quantitatively compared with one popular foreground/background separation technique.

关键词： Clustering algorithms Lighting Labeling Smoothing methods Image color analysis computer vision pattern recognition Image segmentation Internet Layout

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Stroke Level User-Adaptation for Stroke Order Free Online Handwriting recognition

Stroke Level User-Adaptation for Stroke Order Free Online Ha...

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International Workshop on Frontiers in Handwriting recognition

作者： D. Dutta A. Roy Chowdhury U. Bhattacharya S.K. Parui Dept. of Information Technology Heritage Institute of Technology Kolkata India Heritage Institute of Technology Kolkata West Bengal IN Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this article, a novel lightweight user-adaptive online handwriting recognition scheme has been presented. The present recognition approach is stroke order free. It is based on prior identification of the set of various strokes of different shapes used in writing the characters of the underlying alphabet utilizing a representative sample database. In this approach a very small number of prototypes of each stroke shape is used along with certain weighted DTW distance based nearest neighbour classifier to recognize the strokes in the input character. Individual characters are identified using a look-up table (LUT) each row of which corresponds to one character composed of a distinct set of stroke shapes. This LUT is formed using the representative sample set of the underlying character set. If a stroke does not find a close match in the training set or if the set of strokes for an input character does not find a corresponding entry in the LUT, user adaptation takes place using a modified Learning Vector Quantization (LVQ) method. The proposed scheme has been implemented in an Android-based Bangla handwriting recognizer [1] for handheld devices and its recognition performance is encouraging.

关键词： Handwriting recognition Character recognition Libraries Prototypes Table lookup Accuracy Writing

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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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A New U-Net Based System for Multi-Cultural Wedding Image Classification

SSRN

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SSRN 2023年

作者： Shivakumara, Palaiahnakote Kumar, C. Pavan Nemade, Jagrut J. Michael, Kshitiz Kumar, Akash Anami, Basavaraj S. Pal, Umapada Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Indian Institute of Information Technology Dharwad India KLE Institute of Technology India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Use of social media for communication, sharing expressing views, broadcasting news, threatening and blackmailing has become an integral part of society. One such activity is understanding multi-cultural wedding images uploaded on social media. This paper presents a novel method based on the combination of U-Net, Convolutional Neural Network and Random Forest for classification of multicultural wedding images. In the case of wedding images, bride and bridegroom draw the attention of the viewers. This observation led to propose U-Net for segmenting the region of bride and bridegroom in a novel way. Similarly, it is noted that the costumes of bride and bridegroom are vital information for differentiating different cultures. This cue motivated us to extract features using CNN for classification. Since the extracted features using CNN are capable of discriminating images of different classes, we propose a simple and effective Random-Forest for Multicultural Wedding Image Classification. The efficiency of the proposed model is demonstrated by testing it on our own dataset of six multi-cultural wedding classes and standard dataset of wedding and non-wedding images classes. Experimental results on both the datasets show that the proposed model outperforms the state-of-the-art models in terms of average classification rate © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

arXiv

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arXiv 2021年

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

In medical image analysis, the cost of acquiring high-quality data and their annotation by experts is a barrier in many medical applications. Most of the techniques used are based on supervised learning framework and need a large amount of annotated data to achieve satisfactory performance. As an alternative, in this paper, we propose a self-supervised learning (SSL) approach to learn the spatial anatomical representations from the frames of magnetic resonance (MR) video clips for the diagnosis of knee medical conditions. The pretext model learns meaningful spatial context-invariant representations. The downstream task in our paper is a class imbalanced multi-label classification. Different experiments show that the features learnt by the pretext model provide competitive performance in the downstream task. Moreover, the efficiency and reliability of the proposed pretext model in learning representations of minority classes without applying any strategy towards imbalance in the dataset can be seen from the results. To the best of our knowledge, this work is the first work of its kind in showing the effectiveness and reliability of self-supervised learning algorithms in class imbalanced multi-label classification tasks on MR videos. © 2021, CC0.

关键词： Magnetic resonance imaging

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Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Recognizing Bengali Word Images - A Zero-Shot Learning Persp...

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International Conference on pattern recognition

作者： Sukalpa Chanda Daniël Haitink Prashant Kumar Prasad Jochem Baas Umapada Pal Lambert Schomaker Østfold University College Norway Faculty of Science and Engineering. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen The Netherlands Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training. Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types. This work investigates recognition of word images written in Bengali Script in a ZSL framework. The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from various CNN architectures along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters. As per the notion of ZSL framework those 13 basic shapes are termed as “Signature/Semantic Attributes”. The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.

关键词： Training Couplings Image recognition Shape Character recognition

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Word-Wise Handwritten Persian and Roman Script Identification

Word-Wise Handwritten Persian and Roman Script Identificatio...

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International Workshop on Frontiers in Handwriting recognition

作者： Kaushik Roy Alireza Alaei Umapada Pal Department of Computer Science West Bengal State University Kolkata India Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification from bi-script documents written in Persian and Roman. In the proposed scheme, simple but fast computable set of 12 features based on fractal dimension, position of small component, topology etc. are used and a set of classifiers are employed for script identification experiments. We tested our scheme on a dataset of 5000 handwritten Persian and English words and 99.20% of correct script identification is obtained.

关键词： Fractals Support vector machines Kernel Polynomials Training Artificial neural networks Neurons

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A method to generate synthetically warped document image

arXiv

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arXiv 2019年

作者： Garai, Arpan Biswas, Samit Mandal, Sekhar Chaudhuri, Bidyut B. Department of Computer Science and Technology Indian Institute of Engineering Sciences and Technology Shibpur Hawrah West Bengal711103 India Techno India University Kolkata 3 Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

The digital camera captured document images may often be warped and distorted due to different camera angles or document surfaces. A robust technique is needed to solve this kind of distortion. The research on dewarping of the document suffers due to the limited availability of benchmark public dataset. In recent times, deep learning based approaches are used to solve the problems accurately. To train most of the deep neural networks a large number of document images is required and generating such a large volume of document images manually is difficult. In this paper, we propose a technique to generate a synthetic warped image from a flat-bedded scanned document image. It is done by calculating warping factors for each pixel position using two warping position parameters (WPP) and eight warping control parameters (WCP). These parameters can be specified as needed depending upon the desired warping. The results are compared with similar real captured images both qualitative and quantitative way. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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ICFHR 2014 Competition on recognition of On-Line Handwritten Mathematical Expressions (CROHME 2014)

ICFHR 2014 Competition on Recognition of On-Line Handwritten...

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International Workshop on Frontiers in Handwriting recognition

作者： H. Mouchère C. Viard-Gaudin R. Zanibbi U. Garain IRCCyN/IVC LUNAM Université de Nantes Department of Computer Science Rochester Institute of Technology Rochester NY USA Computer Vision and Pattern Recognition (CVPR) Indian Statistical Institute

We present the outcome of the latest edition of the CROHME competition, dedicated to on-line handwritten mathematical expression recognition. In addition to the standard full expression recognition task from previous competitions, CROHME 2014 features two new tasks. The first is dedicated to isolated symbol recognition including a reject option for invalid symbol hypotheses, and the second concerns recognizing expressions that contain matrices. System performance is improving relative to previous competitions. Data and evaluation tools used for the competition are publicly available.

关键词： Feature extraction Training Handwriting recognition Grammar Mathematical model Layout Ink

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