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Proceedings of the Satellite Workshops of icvgip 2021 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Uma Mudenagudi Aditya Nigam Ravi Kiran Sarvadevabhatla Ayesha Choudhary

ISBN: (数字)9789811941368

ISBN: (纸本)9789811941351;9789811941382

this book constitutes peer-reviewed proceedings of satellite workshops of the 12th indian conference on computer vision, graphics, and image processing (icvgip 2021). the book focuses on medical image processing, digital heritage, document analysis and recognition, and computer vision applications. the first part includes submissions on digital archiving and restoration methods with interesting and innovative research components. the second part focuses on medical imaging modalities including MRI, X-ray, CT, imaging in nuclear medicine, medical ultrasound, optical and confocal microscopy, and video and range data images. the third part deals with document analysis and recognition and focuses on text recognition, document layout analysis, understanding, historical and degraded document analysis, datasets, performance evaluation, metrics, etc. the fourth part of this book includes research work from academia and industry across the globe on smart, innovative, and practical applications of computer vision for industrial and societal impact. this book shares innovative ideas, experience and expertise, and ongoing research ideas and will be helpful for researchers and practitioners in academia and industry.

关键词： Signal, image and Speech processing computer Imaging, vision, Pattern Recognition and graphics Data Structures and Information theory Artificial Intelligence

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A finite mixture model based on pair-copula construction of multivariate distributions and its application to color image segmentation 12

A finite mixture model based on pair-copula construction of ...

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8th indian conference on computer vision, graphics and image processing, icvgip 2012

作者： Roy, Anandarup Parui, Swapan K. Roy, Utpal CVPR Unit Indian Statistical Institute 203 B. T. Road Kolkata - 108 India Dept. of Comp. and System Sc. Visva-Bharati University Santiniketan- 731235 India

ISBN: (纸本)9781450316606

this paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. the advantage of such a model is that the margins and the dependence structures are de-coupled from each other. Also, they could be modeled separately. In effect the mixture model (called DVMM) is capable of capturing a broader family of distributions including non-Gaussian models. As an application, we consider the task of color image segmentation in CIE-LUV color space. the process of segmentation could be viewed as an unsupervised clustering of the image pixels. the clusters usually represent the possible segments in the image. Here, the image pixels are assumed to be samples from a DVMM. the expectation maximization algorithm is used to estimate the model parameters. One cluster generally consists of several connected components inside an image. We further note the existence of redundant connected components that represent the boundary of two adjacent components. We here propose a methodology that merges such component pixels to the other two components. We conduct extensive experiments on Berkeley segmentation data set. We take a number of error measures to evaluate the quality of segmentation. On the basis of a comparison with two existing mixture model based segmentation approaches, we find our results encouraging. © 2012 ACM.

关键词： Pixels

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MoCoMER: A Self Supervised Representation Learning for Handwritten Mathematical Expression Recognition 24

MoCoMER: A Self Supervised Representation Learning for Handw...

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15th indian conference on computer vision graphics and image processing

作者： Pramanik, Sandip Mitra, Shree Das, Nibaran Jadavpur Univ Dept CSE Kolkata W Bengal India Indian Inst Informat Technol Guwahati Gauhati India

ISBN: (纸本)9798400710759

this paper presents a self-supervised learning (SSL) based framework, specifically designed for Handwritten Mathematical Expression Recognition (HMER). the proposed approach incorporates a momentum encoding technique and a non-linear projection head into the image encoder to effectively address the common issue of dimensional collapse in self-supervised learning (SSL) methods. Our approach consists of two main steps: first, we use self-supervised pre-training to train the image encoder to obtain strong feature representations from HME images. Subsequently, we fine-tune the model using a Transformer network to predict LaTeX sequences from HME images. the assessment demonstrates that our SSL framework surpasses other existing SSL frameworks as well as several supervised methods in terms of performance. the findings indicate that our approach is highly advanced, emphasizing its potential to enhance the robustness and efficiency of feature representations in HMER tasks. the integration of momentum encoding and a non-linear projection head in the image encoder is shown to enhance the durability and effectiveness of feature representations, leading to superior performance in HMER tasks. Our experiments reveal that our approaches achieve an expression recognition rate (ExpRate) of 62. 17%, 61. 03%, 64. 8% on the CROHME 2014,2016,2019 test datasets respectively. the CROHME 2019 test data achieves the highest ExpRate, which is state-of-the-art (SOTA). this success is achieved by overcoming the challenges of dimensional collapse and leveraging the advantages of both self-supervised and supervised learning.(1)

关键词： HMER SSL Contrastive Learning Momentum Contrast (MoCo) Non-linear projection head Transformer

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LBP-SURF descriptor with color invariant and texture based features for underwater images 12

LBP-SURF descriptor with color invariant and texture based f...

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8th indian conference on computer vision, graphics and image processing, icvgip 2012

作者： Prabhakar, C.J. Praveen Kumar, P.U. Department of P.G. Studies and Research in Computer Science Kuvempu University Shankaraghatta - 577451 Karnataka India

ISBN: (纸本)9781450316606

In this paper, we introduce LBP-SURF, a local image descriptor for underwater environment, which is very efficient to extract color invariant and texture based features of underwater images. the current state-of-the-art feature descriptors viz. SIFT, SURF, DAISY, GLOH and variants are well known techniques for detecting and describing features of objects captured in the out-of-water environment. these standard descriptors have been proven to be the most robust to geometric variations. Nearly, all these geometrical invariant approaches avoid dealing with color images due to the color constancy problem. In underwater images, variation in color is very high compared to variations in geometrical properties due to propagation properties of light. the literature survey reveals that, the texture parameters that remain constant for the scene patch for the whole underwater image sequence. this motivated us to consider texture and color invariant features of underwater images, instead of using the gray-based geometrical invariant features. We normalize the color image using comprehensive color image normalization method to render the color values changed by the various radiometric factors of underwater environment. Our method uses Speeded Up Robust Features (SURF) to detect interest points from the normalized image. the texture features are extracted, and description is stored using Center-Symmetric Local Binary Patterns (CS-LBP) descriptor. the combination of SURF and CS-LBP, called LBP-SURF is evaluated extensively to verify its effectiveness with datasets acquired in underwater environment. © 2012 ACM.

关键词： Color

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No Prompting Frozen Foundation Models: Interactive Medical Volume Segmentation using Continual Test Time Adaptation of Compact Models 24

No Prompting Frozen Foundation Models: Interactive Medical V...

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15th indian conference on computer vision graphics and image processing

作者： Borkar, Kushal Reen, Abhilaksh Singh Jawahar, C. V. Arora, Chetan Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Delhi Delhi India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Automated segmentation of medical image volumes promises to reduce costly medical experts' time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. this paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72x. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64x. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

关键词： Medical image Analysis image Segmentation Human-computer Interaction Continual Learning

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Objective evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform 12

Objective evaluation of noisy multimodal medical image fusio...

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8th indian conference on computer vision, graphics and image processing, icvgip 2012

作者： Singh, Rajiv Khare, Ashish Department of Electronics and Communication University of Allahabad Allahabad India

ISBN: (纸本)9781450316606

Medical image fusion needs proper attention as images obtained from medical instruments are of poor contrast and corrupted by blur and noise due to imperfection of image capturing devices. thus, objective evaluation of medical image fusion techniques has become an important task in noisy domain. therefore, in the present work, we have proposed maximum selection and energy based fusion rules for the evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform (DCxWT). Unlike, traditional real valued wavelet transforms, which suffered from shift sensitivity and did not provide any phase information, DCxWT is shift invariant and provides phase information through its imaginary coefficients. Shift invariance and availability of phase information properties of DCxWT have been found useful for fusion of multimodal medical images. the experiments have been performed over several set of noisy medical images at multiple levels of noise for the proposed fusion scheme. Further, the proposed fusion scheme has been tested up to the maximum level of Gaussian, salt & pepper and speckle noise. Objective evaluation of the proposed fusion scheme is performed with fusion factor, fusion symmetry, entropy, standard deviation and edge information metrics. Results have been shown for two sets of multimodal medical images for the proposed method with maximum and energy based fusion rules, and comparison has been done with Lifting wavelet transform (LWT) and Stationary wavelet transform (SWT) based fusion methods. Comparative analysis of the proposed method with LWT and SWT based fusion methods at different noise levels shows the superiority of the proposed scheme. Moreover, the plots of different fusion metrics against the maximum level of Gaussian, salt & pepper and speckle noise show the robustness of the proposed fusion method against noise. © 2012 ACM.

关键词： image fusion

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Assessment of computational visual attention models on medical images 12

Assessment of computational visual attention models on medic...

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8th indian conference on computer vision, graphics and image processing, icvgip 2012

作者： Jampani, Varun Ujjwal Sivaswamy, Jayanthi Vaidya, Vivek CVIT IIIT-Hyderabad Hyderabad India Medical Image Analysis Lab. GE Global Research Bangalore India

ISBN: (纸本)9781450316606

Several computational visual saliency models have been proposed in the context of viewing natural scenes. We aim to investigate the relevance of computational saliency models in medical images in the context of abnormality detection. We report on two studies aimed at understanding the role of visual saliency in medical images. Diffuse lesions in Chest X-Ray images, which are characteristic of Pneumoconiosis and high contrast lesions such as 'Hard Exudates' in retinal images were chosen for the study. these approximately correspond to conjunctive and disjunctive targets in a visual search task. Saliency maps were computed using three popular models namely Itti-Koch [7], GBVS [3] and SR [4]. the obtained maps were evaluated against gaze maps and ground truth from medical experts. Our results show that GBVS is seen to perform the best (Mdn. ROC area = 0.77) for chest X-Ray images while SR performs the best (ROC area = 0.73) for retinal images, thus asserting that searching for conjunctive targets calls for a more local examination of an image while disjunctive targets call for a global examination. Based on the results of the above study, we propose extensions for the two best performing models. the first extension makes use of top down knowledge such as lung segmentation. this is shown to improve the performance of GBVS to some extent. In the second case the extension is by way of including multi-scale information. this is shown to significantly (by 28.76%) improve abnormality detection. the key insight from these studies is that bottom saliency continues to play a predominant role in examining medical images. © 2012 ACM.

关键词： Ophthalmology

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Semantic Map Injected GAN Training for image-to-image Translation 12th

Semantic Map Injected GAN Training for Image-to-Image Transl...

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12th indian conference on computer vision, graphics, and image processing, icvgip 2021

作者： Kshatriya, Balaram Singh Dubey, Shiv Ram Sarma, Himangshu Chaudhary, Kunal Gurjar, Meva Ram Rai, Rahul Manchanda, Sunny Computer Vision Group Indian Institute of Information Technology Sri City Chittoor India Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India Bangalore India

ISBN: (纸本)9789811941351

image-to-image translation is the recent trend to transform images from one domain to another domain using a generative adversarial network (GAN). the existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Let us refer to the original training as the training for the translation of the input image into the target domain. the injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. the semantic map is only utilized at the training time and is not required at the test time. the experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID, and KID scores after injecting semantic training as compared to the original training. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Concept level discriminant analysis techniques for dimension reduction in image classification tasks 14

Concept level discriminant analysis techniques for dimension...

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9th indian conference on computer vision, graphics and image processing, icvgip 2014

作者： Raman, Ramya K. Sekhar, C. Chandra Department of Computer Science and Engineering Indian Institute of Technology Madras India

ISBN: (纸本)1595930361

A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used to represent the images. In such cases, the reduced dimension is restricted by the number of classes and that reduced dimension may not adequately capture the necessary discriminatory information for classification. We propose a method to perform the discriminant analysis using the concept level labels for local feature vectors extracted from the blocks of an image. the dimension reduction is carried out on the local feature vectors. We consider the indices of the clusters of local feature vectors of all the images as the unnamed concept level labels. As the number of concept level labels is larger than the number of class level labels, the reduced dimension for local feature vectors can be higher than the number of class level labels. We consider the Gaussian mixture model based classifiers and support vector machine based classifiers for image classification using the set of local feature vectors representation of images. Results of experimental studies on image classification for MIT-8 and Vogel-6 image datasets demonstrate the effectiveness of proposed concept level discriminant analysis techniques for dimension reduction. Copyright 2014 ACM.

关键词： Discriminant analysis

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Improved Discrepancy based Domain Adaptation Network using combined loss functions and Feature transformations 14

Improved Discrepancy based Domain Adaptation Network using c...

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14th indian conference on computer vision, graphics and image processing, icvgip 2023

作者： Ashly, Ajith Ganesha, Srinivas Damaraju Gopakumar, G. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Kerala Amritapuri690525 India

ISBN: (纸本)9798400716256

Domain shifts are a common problem in computer vision. As a result, a classifier trained on a source domain cannot perform well on a target domain. Due to this, a source classifier trained to differentiate based on a specific distribution cannot classify well on new data from a different distribution. In this work, we have studied different domain adaptation networks for their complexity and performance and have shown that a simple discrepancy based network can be modified to produce a performance on par with the more complex GAN based networks[8]. this is achieved by picking and selectively combining loss functions and preprocessing transformations. We have shown that the proposed method has produced accuracies 77.62% and 81.67% on popular datasets Office-31 and MNIST/MNIST-M, surpassing the highest reported accuracies by 6.47% and 16.67% compared to seminal papers [16][1] in this field. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex networks

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