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CONF ON image MODELING

作者： COOK, GW COMER, ML DELP, EJ Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette 47907-1285 IN United States

ISBN: (纸本)081941137X

The use of mathematical morphology in low- and mid-level image processing and computer vision applications has allowed the development of a class of techniques for analyzing shape information in color images. These techniques have shown to be useful in image enhancement, segmentation, and analysis. In this paper, we develop and test scalable parallel algorithms necessary to implement a class of morphological filters on a parallel computer, specifically, the MasPar MP-1. We examine the issues relative to the parallel implementation of the algorithms and show that real-time enhancement of high resolution color images is possible. © 1993 SPIE. All rights reserved.

关键词： Mathematical morphology

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A two-step fuzzy-Bayesian classification for high dimensional data

A two-step fuzzy-Bayesian classification for high dimensiona...

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15th International Conference on Pattern Recognition, ICPR 2000

作者： Mostafa, Mostafa G.-H. Perkins, Timothy C. Farag, Aly A. Computer Vision and Image Processing Laboratory University of Louisville KY40292 United States

ISBN: (纸本)0769507506

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons with conventional FCM clustering technique and Bayesian classification technique are also presented. Second, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms ore used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results show improvement in the classification accuracy and reliability using the two-step algorithm. © 2000 IEEE.

关键词： Clustering algorithms

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Analytical method for MGRF Potts model parameter estimation

Analytical method for MGRF Potts model parameter estimation

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作者： Ali, Asem M. Farag, Aly A. Gimel'farb, Georgy Computer Vision and Image Processing Laboratory University of Louisville United States Department of Computer Science University of Auckland New Zealand

ISBN: (纸本)9781424421756

This paper proposes a new analytical method for estimating parameters of a homogeneous isotropic Potts model with an asymmetric Gibbs potential function. The model is generalized by including both pairwise and triple cliques. The maximum likelihood estimates of the cliques potentials are obtained by a further elaboration of the approximate analytical estimator proposed in [8]. Experiments with synthetic textures have shown that our potential estimates are more accurate and practicable than their counterparts obtained with classical methods. © 2008 IEEE.

关键词： Potts model

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AN INVESTIGATION OF JPEG image AND VIDEO COMPRESSION USING PARALLEL processing

AN INVESTIGATION OF JPEG IMAGE AND VIDEO COMPRESSION USING P...

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1994 IEEE International Conference on Acoustics, Speech and Signal processing

作者： COOK, GW DELP, EJ Computer Vision Ond Image Processing Laboratory School of Electrical Engineering Purdue University West Lafayette IN 47907-1285 United States

The problem inherent with any digital image (or digital video) system is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop more complex algorithms that compress images to lower data rates with better fidelity. One approach that can be used to increase the execution speed of these complex algorithms is through the use of parallel processing. In this paper we address one aspect of the parallel implementation of the JPEG still image compression standard on the MasPar MP-1, a massively parallel SIMD computer. We develop a novel byte alignment algorithm used to efficiently output compressed data from the parallel system. © 1994 IEEE.

关键词： image compression

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Shape-based MRI data segmentation in the presence of intensity inhomogeneities using level sets

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International Congress Series 2004年第C期1268卷 213-218页

作者： Hassan, Hossam Farag, Aly A. Electrical and Computer Engineering Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 403 Lutz Hall Building Universit United States

Magnetic resonance imaging (MRI) intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. The removal of the spatial intensity inhomogeneities from MR images is difficult because the inhomogeneities could change with different MRI acquisition parameters from patient to patient and from slice to slice. We propose a shape-based classification technique to overcome this problem using the level set approach. A signed distance function is dedicated to describe the model of the tissue of interest. Then, a partial differential equation (PDE) is derived to describe the evolution of an observed structure. At each iteration, the observed structure is registered with the prior shape model. The algorithm is used at different levels of noise and intensity inhomogeneities showing a good accuracy. © 2004, Elsevier Inc. All rights reserved.

关键词： Bias field image segmentation Level set segmentation MR imaging PDE

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Brain MR image segmentation using tabu search and hidden markov random field model

Brain MR image segmentation using tabu search and hidden mar...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Nanda, P.K. Patra, D. Pradhan, A. Department of Electrical Engineering Image Processing and Computer Vision Laboratory National Institute of Technology Rourkela Orissa 769008 India

ISBN: (纸本)0972741216

In this paper, we propose a hybrid Tabu Expectation Maximization (TEM) Algorithm for segmentation of Brain Magnetic Resonance (MR) images in both supervised and unsupervised framewrok. Gaussian Hidden Markov Random Field (GHMRF) is used to model the available degraded image. In supervised framework, the apriori image MRF model parameters as well as the GHMRF model parameters are assumed to be known. The class labels are estimated using the Maximum a Posteriori (MAP) estimation criterion. In unsupervised framework, the problem of model parameter estimation and label estimation is formulated in Expectation Maximization (EM) framework. The labels are estimated using the proposed Tabu Search algorithm while the model parameters are the maximum likelihood estimates. Our proposed algorithm yields results with arbitrary initial paramater set and thus overcomes the problem of proper choice of initial parameters. The results obtained are comparable with the results obtained by using the algorithm proposed by Zhang ***. [15] , where the Iterated Conditional Mode (ICJM) algorithm is used for computing the MAP estimates. Copyright © IICAI 2005.

关键词： Tabu search

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Random-valued impulse denoising using a fast l 1-minimization-based image inpainting technique

Random-valued impulse denoising using a fast l 1-minimizatio...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Kalita, Mayuri Deka, Bhabesh Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

In this chapter, an image inpainting approach based on l 1-norm regularization is presented for the estimation of pixels corrupted by the random-valued impulse noise. It is a two-stage reconstruction scheme. First, a reasonably accurate random-valued impulse detection scheme is applied to detect the corrupted pixels. Next, the corrupted pixels are treated as missing pixels and replaced by using an image inpainting technique. The inpainting method is based on the fast iterative shrinkage thresholding algorithm (FISTA). The proposed method is fast and experimental results show that it is robust to non-Gaussian and nonadditive degradations like the random-valued impulse noise. It also outperforms similar random-valued impulse denoising schemes in terms of computational complexity while preserving the image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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Multi-modal medical volumes fusion by surface matching

Multi-modal medical volumes fusion by surface matching

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5th International Symposium on Signal processing and Its Applications, ISSPA 1999

作者： Eldeib, Ayman M. Yamany, Sameh M. Farag, Aly Computer Vision and Image Processing Laboratory University of Louisville Department of Electrical Engineering Louisville KY 40292 United States

ISBN: (纸本)1864354518

Presents a fast, six degrees of freedom, registration technique to accurately locate the position and orientation of medical volumes (e.g. CT, MRI) with respect to each other for the same patient. The technique uses surface registration and maximization of mutual information. We have developed a novel technique for surface registration which produces highly accurate results when registering two different volumes of the same individual generated from the same modality, such as preoperative MR and intraoperative MR volumes. In case surface registration is not able to accurately register the different volumes, the result is enhanced by multi-modal volume registration. The gain of this combination is to have an accurate alignment and to reduce time needed for registration. For the multi-modal volume registration, the maximization of mutual information (MI) as a matching criterion is used based on genetic algorithms (GA) as a search engine. Our results demonstrate that our registration technique allows for fast, accurate, robust and completely automatic registration of multimodality medical volumes. © 1999 IEEE.

关键词： Genetic algorithms

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Restoration of out of focus barcode images using Wiener filter

Restoration of out of focus barcode images using Wiener filt...

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5th International Conference on Signal-image Technology and Internet-Based Systems, SITIS 2009

作者： Addiati, D. Sheikh, U.U. Abu Bakar, S.A.R. Computer Vision Video and Image Processing Laboratory Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Malaysia

ISBN: (纸本)9780769539591

Barcode capturing using embedded camera in mobile phone is prone to producing images with out of focus blurring. Barcode decoding systems usually fail to decode barcodes from an image that is affected by image blurring. With macro function, users are able to capture sharp barcodes. Unfortunately, low end and mid range cameras usually do not have macro function which enables close range image capturing. To improve barcode recognition, we propose a technique to restore barcode images before the decoding process. First, we identify and calculate the point spread function (PSF) parameter and radius of the blur in Cepstrum domain. Then we restore the degraded image using Wiener filter. Experiments on both synthetic and real images show that our technique is adept in estimating the radius of the blur. The Wiener filter gives satisfactory result when it is used to restore blurred images with the acquired PSF from the preceding step. © 2009 IEEE.

关键词： image reconstruction

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Weighted wavelet tree sparsity regularization for compressed sensing magnetic resonance image reconstruction

Weighted wavelet tree sparsity regularization for compressed...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Deka, Bhabesh Datta, Sumit Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

Compressed sensing in magnetic resonance imaging (CS-MRI) improves the MRI scan time by acquiring only a few k-space samples and then reconstructs the image using a nonlinear procedure from the highly undersampled measurements. Besides the standard wavelet sparsity, MR images are also found to exhibit tree sparsity across various scales of the wavelet decomposition which are generally modeled as overlapping group sparsity. In this chapter, we propose a novel iteratively weighted wavelet tree sparsity based CS-MRI reconstruction technique to estimate MR images from highly undersampled Fourier measurements. Simulations on various real MR images show that the proposed technique offers significant improvements compared to the state-of-the-art either in terms of visual quality or k-space measurements with the same reconstruction time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Magnetic resonance imaging

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