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Anatomical model-guided segmentation of brain MR image sequences 14

Anatomical model-guided segmentation of brain MR image seque...

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

作者： Mondal, Prasenjit Mukhopadhyay, Jayanta Sural, Shamik Bhattacharyya, Pinak Pani Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Benagl India School of Information Technology Indian Institute of Technology Kharagpur West Benagl India Department of Radiology Quadra Medical Services Pvt. Ltd. Kolkata India

ISBN: (纸本)1595930361

this paper presents a method for segmentation of human brain magnetic resonance (MR) image sequences based on a 3D human brain model (triangulated mesh). the brain model is composed of four components, namely, cerebrum, cerebellum, brain stem and pituitary gland. Synthesized image sequences are extracted from the model at regular intervals for sagittal and coronal views as done in MR imaging. To align a series of real MR images with these synthesized cross-sections, an efficient dynamic programming based computational technique has been used that obtains the optimal synthesized cross-section sequence corresponding to the series of MR images. For automatic segmentation of anatomical structures from the MR images, each aligned synthesized cross-section is overlaid on the corresponding physical MR image by carrying out appropriate geometric transformation. this transformation produces model guided boundaries for four segments corresponding to cerebrum, cerebellum, brain stem and pituitary gland. Subsequently, these initial contours are further refined by the method of active contouring, which provides segmentation of 3D MR images into the above mentioned four parts. the proposed method compares well with the recently proposed Charged Fluid Model (CFM) based approach and level set segmentation method in terms of accuracy at a significantly lower computational cost. Copyright 2014 ACM.

关键词： image segmentation

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A novel intelligent multiple watermarking schemes for the protection of the information content of a document image 10

A novel intelligent multiple watermarking schemes for the pr...

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

作者： Chetan, K.R. Nirmala, S. Department of CSE JNN College of Engineering ShimogaKarnataka India

Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. the sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. the results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image. © Springer International Publishing AG 2017.

关键词： image watermarking

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Removing structural noise in handwriting images using deep learning 14

Removing structural noise in handwriting images using deep l...

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

作者： Chen, Gang Srihari, Sargur N. Dept. of Computer Science and Engineering University at Buffalo State of University of New York BuffaloNY14260 United States

ISBN: (纸本)1595930361

Since handwriting recognition is very sensitive to structural noise, like superimposed objects such as straight lines and other marks, it is necessary to remove noise in a preprocessing stage before recognition. Although numerous denoising approaches have been proposed, it remains a challenge. the difficulties are due to non-locality of structural noise and hard discernment between spurious and the meaningful regions. We propose a supervised approach using deep learning to remove structural noise. Specifically, we generalize the deep autoencoder into the deep denoising autoencoder (DDAE), which consists in training a neural network with noisy and clean pairs to minimize cross-entropy error. Inspired by recurrent neural networks, we introduce feedback loop from the output to enhance the "repaired" image well in the reconstruction stage in our framework. We test the DDAE model on three handwritten image data sets, and show advantages over Wiener filter, robust Boltzmann machines and deep autoencoder. Copyright 2014 ACM.

关键词： Recurrent neural networks

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Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance 12

Wavelet-based contrast enhancement of dark images using dyna...

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

作者： Chouhan, Rajlaxmi Jha, Rajib Kumar Biswas, Prabir Kumar Indian Institute of Technology Kharagpur India Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9781450316606

In this paper, a dynamic stochastic resonance (DSR)-based technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain. Traditionally, the performance of a stochastic resonance (SR)-based system is improved by addition of external noise. However, in the proposed DSR-based approach, the internal noise of an image has been utilized for the purpose of contrast enhancement. the degradation due to inadequate illumination is treated as noise, and is used to produce a noise-induced transition of the image from a low-contrast state to a high-contrast state. Stochastic resonance is induced in the approximation and detail coefficients in an iterative fashion, producing an increase in variance and mean of the coefficient distribution. Optimal output response is ensured by selection of optimal of bistable system parameters. An iterative algorithm is followed to achieve target value of performance metrics, such as relative contrast enhancement factor (F), perceptual quality measures (PQM), and color enhancement factor (CEF), at minimum iteration count. When compared with the existing SR-based and non SR-based enhancement techniques in spatial and frequency domains, the proposed technique is found to give noteworthy performance in terms of contrast enhancement, perceptual quality, as well as colorfulness. © 2012 ACM.

关键词： Discrete wavelet transforms

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Wireless Networks and Computational Intelligence 1

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丛书名： Communications in computer and Information Science

1000年

作者： K. R. Venugopal L. M. Patnaik

ISBN: (数字)9783642316869

ISBN: (纸本)9783642316852

this book constitutes the refereed proceedings of the 6th International conference on Information processing, ICIP 2012, held in Bangalore, India, in August 2012. the 75 revised full papers presented were carefully reviewed and selected from 380 submissions. the papers are organized in topical sections on wireless networks; image processing; pattern recognition and classification; computer architecture and distributed computing; software engineering, information technology and optimization techniques; data mining techniques; computer networks and network security.

关键词： computer Communication Networks computer Imaging, vision, Pattern Recognition and graphics Information Storage and Retrieval Systems and Data Security Artificial Intelligence Information Systems Applications (incl. Internet)

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Semantic multinomial representation for scene images using CNN-based pseudo-concepts and concept neural network 6th

Semantic multinomial representation for scene images using C...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Pradhan, Deepak Kumar Gupta, Shikha thenkanidiyoor, Veena Aroor Dinesh, Dileep Department of Computer Science and Engineering National Institute of Technology Goa PondaGoa401403 India School of Computing and Electrical Engineering Indian Institute of Technology Mandi MandiH.P.175001 India

ISBN: (纸本)9789811300196

For challenging visual recognition tasks such as scene classification and object detection there is a need to bridge the semantic gap between low-level features and the semantic concept descriptors. this requires mapping a scene image onto a semantic representation. Semantic multinomial (SMN) representation is a semantic representation of an image that corresponds to a vector of posterior probabilities of concepts. In this work we propose to build a concept neural network (CoNN) to obtain the SMN representation for a scene image. An important issue in building a CoNN is that it requires the availability of ground truth concept labels. In this work we propose to use pseudo-concepts obtained from feature maps of higher level layers of convolutional neural network. the effectiveness of the proposed approaches are studied using standard datasets. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantics

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Automatic extraction of road segments from high resolution satellite images - A region and boundary based approach 12

Automatic extraction of road segments from high resolution s...

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

作者： Banerjee, Biplab Buddhiraju, Siddharth Mohan, B. Krishna Satellite Image Analysis Lab. CSRE IIT Bombay Powai Mumbai India Electrical Engineering Dept. IIT Bombay Powai Mumbai India

ISBN: (纸本)9781450316606

the problem of road segment extraction from high resolution satellite or aerial images has been considered in this paper. Efficient extraction of road segments is a difficult task due to the problems regarding image acquisition, local road width and orientation etc. A novel method of road region extraction using local spectral and geometrical properties has been proposed here. the image is first segmented using mean-shift based approach. Edge preserving smoothing is performed beforehand to preserve the sharpness of the object boundaries. then a rejection stage using chord distribution technique is considered to remove dominant non-road regions. It is followed by a cost optimization step based on a region following grid graph to efficiently extract the road segments. A further orientation based region linking is performed to overcome problems due to occlusion. Experiments on several high resolution satellite images have established the capability of the proposed method. © 2012 ACM.

关键词： Extraction

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Rejection-Cascade of Gaussians: Real-Time Adaptive Background Subtraction Framework 7th

Rejection-Cascade of Gaussians: Real-Time Adaptive Backgroun...

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

作者： Kiran, B. Ravi Das, Arindam Yogamani, Senthil Navya Paris France Detection Vision Systems Valeo Chennai India Valeo Vision Systems Galway Ireland

ISBN: (纸本)9789811586965

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. there is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians (CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4–5 and 17% average improvement in accuracy over Wallflowers surveillance datasets. the CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder (VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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C-SAW: Self-Supervised Prompt Learning for image Generalization in Remote Sensing 14

C-SAW: Self-Supervised Prompt Learning for Image Generalizat...

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

作者： Bhattacharya, Avigyan Singha, Mainak Jha, Ankit Banerjee, Biplab Indian Institute of Technology Bombay Maharashtra Mumbai India

ISBN: (纸本)9798400716256

We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive zero-shot generalization performance, their effectiveness is limited when dealing with diverse domains during training and testing. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts, which results in a drop in performance while dealing with such multi-domain data. To address these challenges, we propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features. We observe that CLIP's vision encoder struggles to identify contextual image information, particularly when image patches are jumbled up. this issue is especially severe in optical remote sensing images, where land-cover classes exhibit well-defined contextual appearances. To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features. We keep the CLIP backbone frozen and introduce a small set of projectors for both the CLIP encoders to train C-SAW contrastively. Experimental results demonstrate the superiority of C-SAW across multiple remote sensing benchmarks and different generalization tasks. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Optical remote sensing

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Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework 21

Magnetic resonance image reconstruction by nullspace based f...

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

作者： Reddy, P. Sudhakar Raghavendra, B.S. Narasimhadhan, A.V. National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9781450391276

the finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. the reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. the simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.

关键词： Magnetic resonance imaging

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