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Exploring memory and time efficient neural networks for image captioning 6th

Exploring memory and time efficient neural networks for imag...

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

作者： Parameswaran, Sandeep Narayan Visualization and Perception Lab Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai600036 India

ISBN: (纸本)9789811300196

Automatically describing the contents of an image is one of the fundamental problems in artificial intelligence. Recent research has primarily focussed on improving the quality of the generated descriptions. It is possible to construct multiple architectures that achieve equivalent performance for the same task. Among these, the smaller architecture is desirable as they require less communication across servers during distributed training and less bandwidth to export a new model from one place to another through a network. Generally, a deep learning architecture for image captioning consists of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) clubbed together within an encoder-decoder framework. We propose to combine a significantly smaller CNN architecture termed SqueezeNet and a memory and computation efficient LightRNN within a visual attention framework. Experimental evaluation of the proposed architecture on Flickr8k, Flickr30k and MS-COCO datasets reveal superior result when compared to the state of the art. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Recurrent neural networks

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Evidence Based image Selection for 3D Reconstruction 7th

Evidence Based Image Selection for 3D Reconstruction

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

作者： Yadavannavar, Smita C. Prabhu, Varad Vinod Tabib, Ramesh Ashok Patil, Ujwala Mudengudi, Uma KLE Technological University Hubli India

ISBN: (纸本)9789811586965

In this paper, we propose a framework for image selection using evidence theory, towards 3D reconstruction. the process of 3D reconstruction involves image acquisition, image selection, feature extraction, calculating camera parameters, and generation of the point cloud. Of these all, image selection plays a significant role, as it has a significant impact on the final 3D model. However, in large scale 3D reconstruction, image selection based on a single parameter is not sufficient. We measure the similarity between the images using multiple parameters and generate a combined confidence score towards discarding similar images. Experimental results show that the quality of 3D reconstruction is better using images selected by the proposed method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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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...

引用

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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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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Recent Trends in image processing and Pattern Recognition 1

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丛书名： Communications in computer and Information Science

1000年

作者： KC Santosh Aaisha Makkar Myra Conway Ashutosh K. Singh Antoine Vacavant Anas Abou el Kalam Mohamed-Rafik Bouguelia Ravindra Hegadi

ISBN: (数字)9783031530852

ISBN: (纸本)9783031530845

this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.;the 62 full papers included in this book were carefully reviewed and selected from 216 submissions. the papers are organized in the following topical sections:;Volume I:;Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

关键词： computer Imaging, vision, Pattern Recognition and graphics Artificial Intelligence computer System Implementation computer Applications

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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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End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellationg± 13

End-to-End GPU-Accelerated Low-Poly Remeshing using Curvatur...

引用

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

作者： Chatterjee, Preetam Chayan Soni, Ashutosh Bhowmick, Partha Bhowmick, Iit Kharagpur Indian Institute of Technology West Bengal Kharagpur India ANSYS Software Pvt. Ltd Hinjewadi Maharashtra Pune India

ISBN: (纸本)9781450398237

We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in GPU. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a compromised face quality. Hence, instead of doing that, we voxelize the input mesh first. On the voxelized surface, we perform curvature estimation and discretization, Centroidal Voronoi Tessellation (CVT), and Delaunay triangulation to generate the output mesh. To control the vertex count, we exploit the curvature map, such that sufficiently large triangles constitute the low-curvature regions, whereas appropriately small triangles build up the high-curvature portions. We produce low-poly meshes at various levels of detail and measure their quality using Hausdorff error. We test our algorithm on different 3D scenes, and they are particularly chosen because they contain multiple objects with varied topologies. Upon testing on these datasets, our algorithm successfully achieves very low-poly approximation with a reasonable mesh quality, and at a reasonably faster pace, thus establishing its efficacy for mesh simplification. © 2022 ACM.

关键词： graphics processing unit

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Secret image sharing using fractal codes 14

Secret image sharing using fractal codes

引用

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

作者： Acharya, Aniruddha Babu, R. Venkatesh Video Analytics Laboratory Supercomputer Education Research Center Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

Secret sharing is a well known problem of cryptography. A secret has to be shared among a group of N people, in such a way that only when at least K of them use their shares, the secret is revealed. A special case of this problem is secret image sharing, where the secret is an image. Prior work on image sharing has predominantly been visual secret sharing on binary images, where decoding is done by stacking the shares and viewing them. Some of the works address gray scale and color image sharing too. We target secret image sharing for gray scale images and propose a novel solution based on fractal encoding and decoding. the main advantage of our method as compared to the other schemes is that the space complexity of the shares is O(1). that is the sum total of space required for all the shares is independent of N. We have not seen this property in any of the previous works of image sharing. We achieve this by avoiding the use of images as shares, and using partial and modified fractal codes instead. Moreover, our method implicitly does compression along with image sharing. the proposed algorithm has decoding time complexity O(N), which is the state of the art. Experiments have shown that the proposed method is robust to security attacks. Copyright 2014 ACM.

关键词： Binary images

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