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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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Interpretive self-supervised pre-training: Boosting performance on visual medical data 21

Interpretive self-supervised pre-training: Boosting performa...

引用

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

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Kolkata West Bengal Kolkata India Indian Institute of Technology West Bengal Kharagpur India

ISBN: (纸本)9781450391276

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, their application on medical data has been limited. In this work, we have proposed a novel loss function and derive it's asymptotic lower bound. We have also shown that self-supervised pre-training with the proposed loss function helps in surpassing the supervised baseline on the downstream task. We have also shown that the self-supervised pre-training helps a model in learning better representation in general to achieve better performance compared to supervised baselines. We have mathematically derived that the contrastive loss function asymptotically treats each sample as a separate class and works by maximizing the distance between any two samples and this helps to get better performance. Finally, through exhaustive experiments, we demonstrate that self-supervised pre-training helps to surpass the performance of fully supervised models on downstream tasks. © 2021 ACM.

关键词： Learning algorithms

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Cardiac ultrasound image enhancement using tissue selective total variation regularization 10

Cardiac ultrasound image enhancement using tissue selective ...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Mishra, Deepak Chaudhury, Santanu Sarkar, Mukul Soin, Arvinder Singh Indian Institute of Technology Delhi New Delhi India Medanta Hospital Gurgaon India

Speckle reduction is desired to improve the quality of ultrasound images. However, a uniform speckle reduction from the entire image results in loss of important information, especially in cardiac ultrasound images. In this paper, a tissue selective total variation regularization approach is proposed for the enhancement of cardiac ultrasound images. It measures the pixel probability of belonging to blood regions and uses it in the total variation framework. As a result, the unwanted speckle from the blood chamber regions is removed and the useful speckle in the tissue regions is preserved. this helps to improve the visible contrast of the images and enhances the structural details. the proposed approach is evaluated using synthetic as well as real images. A better performance is observed as compared to the state-of-the-art filters in terms of speckle region’s signal to noise ratio, structural similarity measure index, figure of merit, and mean square error. © Springer International Publishing AG 2017.

关键词： Speckle

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Digitization of historic inscription images using cumulants based simultaneous blind source extraction 14

Digitization of historic inscription images using cumulants ...

引用

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

作者： Jayanthi, N. Tomar, Ayush Raj, Aman Indu, S. Chaudhury, Santanu Delhi Technological University New Delhi India Indian Institute of Technology New Delhi India

ISBN: (纸本)1595930361

In this paper a novel method to address the problem of enhancement and binarization of historic inscription images is presented. Inscription images in general have no distinction between the text layer and background layer due to absence of color difference and possess highly correlated signals and noise. the proposed technique provides a suitable method to separate the text layer from the historic inscription images by considering the problem as blind source separation which aims to calculate the independent components from a linear mixture of source signals, by maximizing a contrast function based on higher order cumulants. Further, the results are compared with existing ICA based techniques like NGFICA and Fast-ICA. Copyright 2014 ACM.

关键词： Blind source separation

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An Efficient Character Segmentation Algorithm for Connected Handwritten Documents 4th

An Efficient Character Segmentation Algorithm for Connected ...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

作者： Rajput, Vishal Jayanthi, N. Indu, S. Department of Electronics and Communication Engineering PDPM-Indian Institute of Information Technology Design and Manufacturing Jabalpur JabalpurMP482005 India Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811393600

this paper proposes an efficient method of character segmentation for handwritten text. the main challenge in character segmentation of hand-written text is the varied size of each letter in different documents, connected alphabets in a word in cursive writing and the presence of ligatures within an open character. Hence, this paper proposes an adaptive vertical pixel count algorithm to solve the problem of over-segmentation due to the presence of open characters such as ‘w’, ‘v’ and ‘m’. Proposed algorithm works effectively against both the handwritten and standard text. the proposed method is evaluated on IAM and self-created data set. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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Temporal stationarity based prediction method for lossless video coding 14

Temporal stationarity based prediction method for lossless v...

引用

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

作者： Vaishnav, Mohit Chobey, Dinesh Kumar Tiwari, Anil Kumar LNM Institute of Information Technology India Indian Institute of Technology Jodhpur India

ISBN: (纸本)1595930361

this paper aims to propose a noble method to estimate the auto-regressive(AR) coefficients used by least-square(LS) based predictors. Estimation of this LS based predictors is computationally most complex process. this process requires a covariance matrix comprised of chosen causal pixels and also the inverse elements of the same matrix. Computational requirements of this process depends on the number of pixels for which the predictor is trained and also on the order of the predictor. Due to this high complexity, the predictor is not used practically although it provides a high compression ratio. thus, an alternative algorithm, popularly known as LOPT-3D, was proposed in literature. However, the number of pixels required for the estimation of AR parameters are still large, and thereby, making it impracticable for real-time implementations. the proposed method overcomes this limitation by effectively making use of previously estimated AR parameters. Copyright 2014 ACM.

关键词： Covariance matrix

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Enhanced Kernelized Conditional Spatial Fuzzy C Means Algorithm for Noisy Brain MRI Tissue Segmentation 13

Enhanced Kernelized Conditional Spatial Fuzzy C Means Algori...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Halder, Amiya Choudhuri, Rudrajit Sarkar, Apurba St Thomas College of Engineering and Technology West Bengal Kolkata India IIEST Shibpur Howrah West Bengal India

ISBN: (纸本)9781450398237

this paper proposes an Enhanced Kernelized Conditional Spatial Fuzzy C Means (EKCSFCM) clustering algorithm targeted at segmentation of Brain Magnetic Resonance (MR) image data. the approach amalgamates kernel mapping with a spatially relevant fuzzy clustering paradigm for image segmentation. A technique for rank-based consideration of spatial information is introduced and is used along with spatial tuning parameters for controlling neighborhood effects during data point clustering. the kernelized approach and the spatial parameters handle intensity inhomogeneities and strengthen the performance of the algorithm by improving resiliency against noise and other aberrations. For analyzing the robustness of the approach, four volumes of brain MR data, each comprising 71 images are used. these images are also corrupted with noise and inhomogeneities for evaluating the efficiency of different approaches in noisy image segmentation. the experimentation results along with the qualitative and quantitative inferences verify the robustness of the algorithm across a wide variety of test cases. © 2022 ACM.

关键词： image segmentation

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Colour sensitive image segmentation using quaternion Algebra 4th

Colour sensitive image segmentation using quaternion Algebra

引用

4th International conference on computer vision and image processing, CVIP 2019

作者： Maity, Sandip Kumar Biswas, Prabir ARC Document Solution Kolkata India Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789811540141

In colour image segmentation, a few vector filters are useful, and they are not well applicable. the segmentation tools which are still available, not also colour sensitive. In this paper, we present an approach for color sensitive segmentation method by using the color sensitivity property of hyper complex or quaternion algebra. the filter is based on quaternion convolution and impulse response is determined using modified gray centered RGB color cube. this method demands that every color can be segmented by choosing suitable impulse response. © Springer Nature Singapore Pte Ltd 2020.

关键词： Color

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Sparse stereo matching using belief propagation

Sparse stereo matching using belief propagation

引用

IEEE International conference on image processing

作者： Michel Sarkis Klaus Diepold Institute for Data Processing Technische Universität München Germany

Depth from stereo is an important research field in computer vision due to the wide range of its applications. In this work, we present a stereo matching algorithm based on belief propagation (BP). the algorithm is designed to work on sparse images originating from image content adaptive mesh representation techniques. there, an image is approximated with a mesh. the nodes of the mesh are the non-uniform samples which are the ones that form the sparse image. the key issue in the proposed method is to formulate BP such that it matches a sparse left stereo image with a dense right image to obtain a sparse depth map. Moreover, we propose a simple method that recovers the dense disparity map of the scene from the sparse one using the approximating mesh of the image. the results obtained show that the proposed method leads to an average of 40% improvement in the quality of depth maps when compared to existing sparse stereo matching techniques.

关键词： Belief propagation Stereo vision Layout Rendering (computer graphics) computer vision Algorithm design and analysis image reconstruction Cost function Data processing Application software

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Deep Learning Models for Medical image Analysis: Challenges and Future Directions 1

引用

7th International conference on Big Data analytics (BDA)

作者： Agrawal, R. K. Juneja, Akanksha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. this concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computer vision competition. thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. the success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

关键词： Medical image analysis Machine learning Deep Learning computer-aided diagnosis

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Artificially Intelligent Game Framework Based on Facial Expression Recognition 7th

Artificially Intelligent Game Framework Based on Facial Expr...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Patidar, Itisha Modh, Karan Sanjay Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811586965

During gameplay, a player experiences emotional turmoil. In most of the cases, these emotions directly reflect the outcome of the game. Adapting game features based on players’ emotions necessitates a way to detect the current emotional state. Researchers in the area of "video game user research" has studied biometric data as a way to address the diverse characteristics of players, their individual preferences, gameplay expertise, and experiences. Identification of the player’s current state is fundamental for designing a game, which interacts with the player adaptively. In this paper, we present an artificially intelligent game framework with smart features based on automatic facial expression recognition and adaptive game features based on the gamer’s emotion. the gamer’s emotions are recognized at run-time during gameplay using Deep Convolutional Neural Networks (CNN), and the game is adapted accordingly to the emotional condition. Once identified, these features directly modify critical parameters of the underlying game engine to make the game more exciting and challenging. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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