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International Conference on Document Analysis and recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratory of Computer Science (LI) Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.

关键词： Biomedical imaging Biomedical measurement

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Endafd: An End-to-End System for Ecg Based Atrial Fibrillation Detection

SSRN

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SSRN 2022年

作者： Basak, Shubham Das, Sudip Dasgupta, Subhrajyoti Bhattacharya, Ujjwal Narayan, Pradeep Computer Vision and Pattern Recognition Unit Indian Statistical Institute WB Kolkata India Rabindranath Tagore International Institute of Cardiac Sciences WB Kolkata India

Atrial fibrillation is one of the most common arrhythmias with significant clinical implications. When it comes to detecting and categorising different types of heart diseases, machine learning and deep learning techniques have shown great results. There is a tendency to use convolution-based neural networks, as they have been specifically designed to categorise and recognise images. Image classification entails extracting image attributes in order to identify certain patterns in the dataset. The use of a basic ANN for this classifications would be computationally costly due to the large number of trainable parameters. Traditional convolution-based detection approaches, on the other hand, occasionally provided skewed findings since they had to integrate all 12 ecg leads to acquire the result. They will occasionally overlook the findings of a single lead abnormality if no other leads are shown to be defective. However, in the instance of a transformer, they take a single lead value at a time and check to see if there is any irregularity within that lead. Although the training may not always be successful owing to the small number of input samples, the results are typically rigged in favor of the negative since the number of negative samples is considerably larger than the positive ones We need a way to generate a larger number of balanced samples via the model, and then train the model to evaluate the original input. This is where the model comes in. A unique end-to-end model is suggested, consisting of two vision transformers connected by a generative adversarial network. © 2022, The Authors. All rights reserved.

关键词： Diseases

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Flexible Sequence Matching Technique: Application to Word Spotting in Degraded Documents

Flexible Sequence Matching Technique: Application to Word Sp...

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International Workshop on Frontiers in Handwriting recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratoire d'Informatique Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a new sequence-matching algorithm, called as Flexible Sequence Matching (FSM) algorithm is proposed. FSM combines several abilities of other sequence matching algorithms (especially DTW, CDP and MVM) that could be configured depending on the application domain. Its generality and robustness comes from its ability to find sub sequences (as in CDP), to skip outliers inside the match sequences (as in MVM) and to match multiple elements with a single one (as in CDP and DTW). These properties make it extremely suitable for robust word spotting. More precisely, the FSM algorithm has the capability to retrieve a query inside a line or piece of line. This facility is useful as word segmentation process may not work accurately or when only line segmentation information is available. Furthermore, thanks to its skipping capability, that makes the proposed FSM algorithm less sensible to local variations in the spelling of words, and also to local degradation effects. Finally, its multiple matching facilities (many to one and one to many matching) are useful in case of different length of target and query sequences due to the variability in scale factor. We demonstrate the superiority of proposed FSM algorithm in specific cases such as incorrect word segmentation and word level local variations. When different experiments were performed using handwritten George Washington dataset and also on historical typewritten document images, quite promising results were obtained.

关键词： Feature extraction Algorithm design and analysis Image segmentation computer architecture Robustness pattern recognition Dynamic programming

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TIC: Text-Guided Image Colorization

arXiv

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arXiv 2022年

作者： Ghosh, Subhankar Roy, Prasun Bhattacharya, Saumik Pal, Umapada Blumenstein, Michael Faculty of Engineering and IT University of Technology SydneyNSW Australia The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Image colorization is a well-known problem in computer vision. However, due to the ill-posed nature of the task, image colorization is inherently challenging. Though several attempts have been made by researchers to make the colorization pipeline automatic, these processes often produce unrealistic results due to a lack of conditioning. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To the best of our knowledge, this is one of the first attempts to incorporate textual conditioning in the colorization pipeline. To do so, we have proposed a novel deep network that takes two inputs (the grayscale image and the respective encoded text description) and tries to predict the relevant color gamut. As the respective textual descriptions contain color information of the objects present in the scene, the text encoding helps to improve the overall quality of the predicted colors. We have evaluated our proposed model using different metrics and found that it outperforms the state-of-the-art colorization algorithms both qualitatively and quantitatively. © 2022, CC BY.

关键词： Pipelines

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MMC: Multi-Modal Colorization of Images using Textual Descriptions

arXiv

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arXiv 2023年

作者： Ghosh, Subhankar Bhattacharya, Saumik Roy, Prasun Pal, Umapada Blumenstein, Michael Faculty of Engineering and IT University of Technology SydneyNSW Australia The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Indian Institute of Technology Kharagpur India

Handling various objects with different colors is a significant challenge for image colorization techniques. Thus, for complex real-world scenes, the existing image colorization algorithms often fail to maintain color consistency. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To do so, we have proposed a deep network that takes two inputs (grayscale image and the respective encoded text description) and tries to predict the relevant color components. Also, we have predicted each object in the image and have colorized them with their individual description to incorporate their specific attributes in the colorization process. After that, a fusion model fuses all the image objects (segments) to generate the final colorized image. As the respective textual descriptions contain color information of the objects present in the image, text encoding helps to improve the overall quality of predicted colors. In terms of performance, the proposed method outperforms existing colorization techniques in terms of LPIPS, PSNR and SSIM metrics. © 2023, CC BY.

关键词： Color

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Color Feature Based Approach for Determining Ink Age in Printed Documents

Color Feature Based Approach for Determining Ink Age in Prin...

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International Conference on pattern recognition

作者： Biswajit Halder Utpal Garain Department of Information Technology Mallabhum Institute of Technology Bisnupur West Bengal India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE magazines published in between 1930's and 70's (five decades). Test results show that a viable framework for involving machines in assisting human experts for determining age of printed documents.

关键词： Ink Image color analysis Feature extraction Pixel Forensics Artificial neural networks Accuracy

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A Statistics and Local Homogeneity Based Color Edge Detection Algorithm

A Statistics and Local Homogeneity Based Color Edge Detectio...

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International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom

作者： Soumya Dutta Bidyut B. Chaudhuri Department of Electronics and Communication Netaji Subhash Engineering College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A novel color edge detection algorithm is proposed in this paper. On the basis of standard deviation calculation of pixels the discontinuity among the pixels are detected. Then the image is segmented into a binary image with a fixed threshold where black pixels signify homogeneous region and white pixels signify edges. Finally, a thinning technique is applied to extract thin edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Statistics Image edge detection Image processing Object recognition Shape measurement Image segmentation Pixel pattern recognition Area measurement computer vision

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An approach for character segmentation of handwritten Bangla and devanagari script

An approach for character segmentation of handwritten Bangla...

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IEEE International Advance Computing Conference, IACC

作者： Anmol J Bhattad Bidyut B. Chaudhuri Electronics & Communication Engineering National Institute of Technology Karnataka Surathkal Mangalore India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479980482

In this paper a scheme for segmentation of unconstrained handwritten Devanagari and Bangla words into characters and its sub-parts is proposed. Firstly, the region above headline is identified by counting the number of white to black transitions in each row, which followed by its separation. Then the characters are segmented using fuzzy logic. For each column, the inputs to the fuzzy system are the location of first white pixel, thickness of the first black stroke, count of white pixels, and the run length count of white pixels.

关键词： Indexes Robustness

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A Color Edge Detection Algorithm in RGB Color Space

A Color Edge Detection Algorithm in RGB Color Space

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International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom

ISBN: (纸本)9781424451043

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： Image edge detection Color Image processing Object recognition Shape measurement pattern recognition Area measurement computer vision Image segmentation Image coding

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Hindi Off-Line Signature Verification

Hindi Off-Line Signature Verification

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International Workshop on Frontiers in Handwriting recognition

作者： Srikanta Pal Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Gold Coast Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Handwritten Signatures are one of the widely used biometrics for document authentication as well as human authorization. The purpose of this paper is to present an off-line signature verification system involving Hindi signatures. Signature verification is a process by which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite of substantial research in the field of signature verification involving Western signatures, very little attention has been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this paper, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, has been investigated. The gradient and Zernike moment features were employed and Support Vector Machines (SVMs) were considered for verification. To the best of the authors' knowledge, Hindi signatures have never been used for the task of signature verification and this is the first report of using Hindi signatures in this area. The Hindi signature database employed for experimentation consisted of 840 (35×24) genuine signatures and 1050 (35×30) forgeries. An encouraging accuracy of 7.42% FRR and 4.28% FAR were obtained following experimentation when the gradient features were employed.

关键词： Feature extraction Forgery Databases Support vector machines Training Testing

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