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non-parametric and unsupervised Bayesian classification with Bootstrap sampling

IMAGE AND VISION COMPUTING 2004年第1期22卷 1-8页

作者： Zribi, M Univ Littoral Cote Opale Maison Rech Blaise Pascal LASL UPRES 2600 F-62228 Calais France

In this paper, we propose a non-parametric and unsupervised Bayesian classification based on the principle of Bootstrap sampling (BS) which reduces the dependence effect of pixels in real images, and reduces the classification time. Given an original image, we randomly select a small representative set of pixels. Then, a non-parametric expectation-maximization (NEM) algorithm is used for image segmentation. The non-parametric aspect comes from the use of the orthogonal probability density function (pdf) estimation, which is reduced to the estimation of the first Fourier coefficients (FC's) of the pdf with respect to a given orthogonal basis. The results we obtain show that the BS method gives better results than the classical one, both in the quality of the segmented image and the computing time. (C) 2003 Elsevier B.V. All rights reserved.

关键词： non-parametric and unsupervised Bayesian classification bootstrap sampling non-parametric expectation-maximization orthogonal probability density functions

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non-parametric and region-based image fusion with Bootstrap sampling

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INFORMATION FUSION 2010年第2期11卷 85-94页

作者： Zribi, Mourad Univ Littoral Cote dOpale LASL UPRES 2600 F-62228 Calais France

image fusion refers to the techniques that integrate complementary information from multiple image sensors' data in a way that makes the new images more suitable for human Visual perception and reduces computation processing tasks. In this paper, we propose a non-parametric and region-based image fusion based oil the Bootstrap sampling (BS) principle, which reduces the dependence effect of pixels in real images and minimizes the fusion time. Given an original image, we randomly select a small representative set of pixels In the statistical image formation model, image sensors are described as the true scene corrupted by additive non-Gaussian distortion Then, a non-parametric expectation-maximization (NEM) algorithm would be used to estimate both the model parameters and the fused image. The non-parametric aspect comes from the use of the orthogonal series' estimator. Obtained results show that the BS method gives better results than the classical one, both for fused image quality as well as for computation time. (C) 2008 Elsevier B.V All rights reserved.

关键词： Region-based image fusion Bootstrap sampling non-parametric expectation-maximization Orthogonal series estimator

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