Validation of a parallel genetic algorithm for image reconstruction from projections

Kabos, Peter; Mirzaei, Siroos

doi:10.1016/s0743-7315(03)00019-4

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…Inversely, with our method the calibration step is not necessary which allows the acquisition of images "on a fly". In addition, EA-based methods can easily be parallelized [20,29], which permits to reduce the computing time. 40 60 80 100 120 140 160 180 76 78 80 82 84 86 88 90 92 94 (b1)).…”

Section: Comparison With Faugeras-toscanimentioning

confidence: 99%

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Koch

Dipandà

Bourgeois-Republique

2010

Multimed Tools Appl

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In this paper, we propose an original evolutionary-based method for 3D panoramic reconstruction from an uncalibrated stereovision system (USS). The USS is composed of five cameras located on an arc of a circle around the object to be analyzed. The main originality of this work concerns the process of the calculation of the 3D information. Actually, with our method, 3D coordinates are directly obtained without any prior estimation of the fundamental matrix. The method operates in two steps. Firstly, points of interest are detected in pairs of images acquired by two consecutive cameras of the USS are matched. And secondly, using evolutionary algorithms, we jointly compute the transformed matrix between the two images and the respective depth of the points of interest. The accuracy of the proposed method is validated through a comparison with the depth values obtained using a traditional method. In order to perform 3D panoramic object reconstruction, the process is repeated for all the pairs of consecutive cameras. The 3D points thus obtained throughout the successive steps of the process which correspond to the different points of interest, are combined in order to obtain a set of 3D points all around the analyzed object.Keywords Panoramic 3D reconstruction . Evolutionary algorithm . Uncalibrated system IntroductionPanoramic 3D reconstruction is a recent area of research which began about 10 years ago. It is used in various domains such as medicine [32], security [30], virtual reality or robotics Multimed Tools Appl (2012) 57:565-586

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Section: Comparison With Faugeras-toscanimentioning

confidence: 99%

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Koch

Dipandà

Bourgeois-Republique

2010

Multimed Tools Appl

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“…When a parallel genetic algorithm was designed and applied to image reconstruction from projections, it performs rather well on noisy data sets, 26 while standard methods to solve a set of equations cannot be applied as the projections are superimposed by statistical noise.…”

Section: Application Of Genetic Algorithms To Xrsmentioning

confidence: 99%

Chemometrics and its applications to x‐ray spectrometry

Luo

2006

X-Ray Spectrometry

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Recent developments in curve fitting, multivariate calibration, and pattern recognition in chemometrics, and their application to x-ray spectrometry, are reviewed. Relatively innovated algorithms, namely genetic algorithms, neural networks and support vector machines, are discussed. Together with the three algorithms, the performances of different algorithms are compared briefly, which mainly includes principal component analysis, partial least-squares regression, factor analysis, cluster analysis, nearest neighbor methods, linear discriminant analysis, linear learning machine, and soft independent modeling of class analogy. In general, the chemometrics methods are superior to the conventional methods, such as Fourier transform and Marquardt-Levenberg algorithms, to a certain extent.

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“…Several evolutionary algorithms (EA) such as genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) have been introduced in recent years in the field of image processing because of their fast computing ability. Several meta heuristics method mainly EA, PSO,GA, DE etc., have been applied for image processing applications [12][13][14][15] including image enhancement problem. GA finds weights and combines four types of nonlinear transform elements [16].…”

Section: Introductionmentioning

confidence: 99%

Natural Image Enhancement Using a Biogeography Based Optimization Enhanced with Blended Migration Operator

Jasper

Shaheema²,

Shiny³

2014

Mathematical Problems in Engineering

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This paper addresses a novel and efficient algorithm for solving optimization problem in image processing applications. Image enhancement (IE) is one of the complex optimization problems in image processing. The main goal of this paper is to enhance color images such that the eminence of the image is more suitable than the original image from the perceptual viewpoint of human. Traditional methods require prior knowledge of the image to be enhanced, whereas the aim of the proposed biogeography based optimization (BBO) enhanced with blended migration operator (BMO) algorithm is to maximize the objective function in order to enhance the image contrast by maximizing the parameters like edge intensity, edge information, and entropy. Experimental results are compared with the current state-of-the-art approaches and indicate the superiority of the proposed technique in terms of subjective and objective evaluation.

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Validation of a parallel genetic algorithm for image reconstruction from projections

Cited by 10 publications

References 4 publications

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Chemometrics and its applications to x‐ray spectrometry

Natural Image Enhancement Using a Biogeography Based Optimization Enhanced with Blended Migration Operator

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