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arXiv 2023年

作者： Shen, Haoran Zhang, Yifu Wang, Wenxuan Chen, Chen Liu, Jing Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Bengali Place Name recognition - Comparative Analysis Using Different CNN Architectures 5th

Bengali Place Name Recognition - Comparative Analysis Using ...

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5th International Conference on computer vision and Image Processing, CVIP 2020

作者： Prasad, Prashant Kumar Banerjee, Pamela Chanda, Sukalpa Pal, Umapada RCC Institute of Information Technology Kolkata India Department of Information Technology Østfold University College Halden Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9789811610912

Optical Character recognition (OCR) has been deployed in the past in different application areas such as automatic transcription and indexing of document images, reading aid for the visually impaired persons, postal automation etc. However, the performance in many cases has not been impressive due to the fact that character segmentation is itself an error-prone and difficult operation, which leads to the poor performance of the system due to erroneous segmentation of characters. Hence, for many applications (like document indexing, Postal automation) where full character-wise transcription is not required, word recognition is the preferred method these days. This article investigates recognition of Bengali place names as word images using 5 different traditional architectures. Experiments on word images (of Bengali place names) from 608 classes were conducted. Encouraging results were obtained in all instances. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Effective Document Image Enhancement Using tokens-to-token Transformer Network

SSRN

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SSRN 2023年

作者： Biswas, Risab Roy, Swalpa Kumar Pal, Umapada Maharashtra Mumbai400066 India Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal735102 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Document image enhancement is a fundamental and important stage for attaining the best performance in any document analysis assignment because there are many degradation situations that could harm document images, making it more difficult to recognize and analyze them. In this paper, we propose to employ a Tokens-to-Token Transformer net- work for document image enhancement, a novel encoder-decoder architecture based on a tokens-to-token vision transformer. The proposed architecture uses a tokens-to-token architecture in the encoder section. Each image is divided into a set of tokens with a de- fined length using the ViT model, which is then applied several times to model the global relationship between the tokens. However, the conventional tokenization of input data does not adequately reflect the crucial local structure between adjacent pixels of the input image, which results in low efficiency. Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokeniza- tion technique to capture this local information from an image for achieving more ef- fective results. Experiments on various DIBCO and H-DIBCO benchmarks demonstrate that the proposed model outperforms the existing CNN and ViT-based state-of-the-art methods. In this research, the primary area of examination is the application of the pro- posed architecture to the task of document binarization. The source code will be made available at https://***/RisabBiswas/T2T-BinFormer. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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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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DegAE: A New Pretraining Paradigm for Low-Level vision

DegAE: A New Pretraining Paradigm for Low-Level Vision

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Conference on computer vision and pattern recognition (CVPR)

作者： Yihao Liu Jingwen He Jinjin Gu Xiangtao Kong Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The University of Sydney

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acqui-sition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (De-gAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tai-lored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词：

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A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement

arXiv

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arXiv 2023年

作者： Biswas, Risab Roy, Swalpa Kumar Pal, Umapada Maharashtra Mumbai400066 India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College West Bengal 736206 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Document image enhancement is a fundamental and important stage for attaining the best performance in any document analysis assignment because there are many degradation situations that could harm document images, making it more difficult to recognize and analyze them. In this paper, we propose T2T-BinFormer which is a novel document binarization encoder-decoder architecture based on a Tokens-to-token vision transformer. Each image is divided into a set of tokens with a defined length using the ViT model, which is then applied several times to model the global relationship between the tokens. However, the conventional tokenization of input data does not adequately reflect the crucial local structure between adjacent pixels of the input image, which results in low efficiency. Instead of using a simple ViT and hard splitting of images for the document image enhancement task, we employed a progressive tokenization technique to capture this local information from an image to achieve more effective results. Experiments on various DIBCO and H-DIBCO benchmarks demonstrate that the proposed model outperforms the existing CNN and ViT-based state-of-the-art methods. In this research, the primary area of examination is the application of the proposed architecture to the task of document binarization. The source code will be made available at https://***/RisabBiswas/T2T-BinFormer. © 2023, CC BY-SA.

关键词： Image enhancement

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Tldsmi: Genetic Algorithm Based Network for Text Localization in Distorted Social Media Images

SSRN

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SSRN 2023年

作者： Palaiahnakote, Shivakumara Kumar, C. Pavan Aggarwal, Pranjal Sharma, Shubham Chandana, Pasupuleti Basavanna, M. Pal, Umapada Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Indian Institute of Information Technology Dharwad India University of Davanagere Karnataka India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper presents a novel model for understanding social image content through text localization. For text localization, we explore Maximally Stable Extremal Regions (MSER) for detecting components, that works by clustering pixels having similar properties. The output of component detection includes several non-text components due to degradations of social media images. To select the best components among many, we explore Genetic Algorithm by convolving different kernels with components, which results in a feature matrix which is further fed to EfficientNet for choosing actual text components. Therefore, the proposed model is called Genetic Algorithm based Network for Text Localization in Distorted Social Media Images (TLDSMI). For evaluating text localization, we consider the images of standard dataset of natural scene by uploading and downloading from different social media platforms, namely, WhatsApp, Telegram and Instagram. The effectiveness of our method is shown by testing on original and distorted standard datasets. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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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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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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Survey of Automatic Plankton Image recognition: Challenges, Existing Solutions and Future Perspectives

arXiv

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arXiv 2023年

作者： Eerola, Tuomas Batrakhanov, Daniel Barazandeh, Nastaran Vatankhah Kraft, Kaisa Haraguchi, Lumi Lensu, Lasse Suikkanen, Sanna Seppälä, Jukka Tamminen, Timo Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory LUT University Yliopistonkatu 34 Lappeenranta53850 Finland Marine Ecology Measurements Finnish Environment Institute Agnes Sjöbergin Katu 2 Helsinki00790 Finland

Planktonic organisms are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand the changes in the environment. Yet, monitoring plankton at appropriate scales still remains a challenge, limiting our understanding of functioning of aquatic systems and their response to changes, which reduces the effectiveness of mitigation measures. Modern plankton imaging instruments can be utilized to sample at high frequencies, producing large amounts of images and enabling novel possibilities to study plankton populations. However, manual analysis of the data is costly, time consuming and expert based, making such approach unsuitable for large-scale application and urging for automatic solutions. The key problem related to the utilization of plankton datasets through image analysis is plankton recognition, i.e., classification of the images. Despite the large amount of research done on automatic plankton recognition, these methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for large-scale operational use. Then, we provide a detailed description of solutions for these challenges proposed in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. For many of the challenges, applicable solutions exist. This is not the case for all and important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes (e

关键词： Plankton

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