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 pos...
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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...
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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 imagethe informati...
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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. Altho...
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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.
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...
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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 a...
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this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in imageprocessing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration w...
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ISBN:
(数字)9783031530852
ISBN:
(纸本)9783031530845
this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in imageprocessing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration withthe 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 imageprocessing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.
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 undersample...
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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 p...
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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 ...
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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 withimage 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.
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