Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images

Costa, Jean-Pierre da; Galland, Frédéric; Roueff, Antoine; Germain, Christian

doi:10.1117/12.890888

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Apart from testing three main novelties as above, we still compare the proposed approach to other new texture segmentation methods, although these researches are not aiming at jacquard fabric images. One of them, as described by Costa et al, 23 is based on von Mises circular distributions. The second method in Lehman 24 is 1D hidden Markov models (1D HMMs).…”

Section: Resultsmentioning

confidence: 99%

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Zhang

Jiang

Yao

et al. 2013

Textile Research Journal

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In this paper, we propose a new approach to intelligently segment jacquard warp-knitted fabric images by combining wavelet texture decomposition, multiresolution Markov random field (MRF) modeling and Bayesian parameter estimation. Firstly, we use a flat scanner to capture eight-bit grayscale images of jacquard fabrics and adopt the Gaussian low-pass filter to decrease pixel variation due to discordant light reflection arising from uneven jacquard fabric surface. To overcome the incapacity of single resolution, multiresolution wavelet texture decomposition is employed, inspired by human visual sense procedure. Next, both intra-scale and inter-scale information are taken into account by the MRF model, in which a modified feature field model of the MRF, containing spatial noise with zero-mean Gaussian distribution, is presented in light of the inherent characteristics of jacquard warp-knitted fabric image. Afterward, an adaptive weighting function is used to weaken the defect of the potential parameter set empirically during the process of parameter estimation and image segmentation. Experimental results, used to verify the performance of the proposed algorithm especially the main novelties, prove that the approach is feasible and applicable.

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Section: Resultsmentioning

confidence: 99%

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Zhang

Jiang

Yao

et al. 2013

Textile Research Journal

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show abstract

“…This paper addresses the complex problem of textured image segmentation that is a subject of importance in various applications [1,2] (see also [3,4]). In practice, observations are often affected by blur (due to finite resolution of observation systems) and by noise (due to various sources of error).…”

Section: Introduction: Motivation and State Of The Artmentioning

confidence: 99%

Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm

Vacar

Giovannelli

2019

EURASIP J. Adv. Signal Process.

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The paper tackles the problem of joint deconvolution and segmentation of textured images. The images are composed of regions containing a patch of texture that belongs to a set of K possible classes. Each class is described by a Gaussian random field with parametric power spectral density whose parameters are unknown. The class labels are modelled by a Potts field driven by a granularity coefficient that is also unknown. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the K textured images in addition to hyperparameters: the signal and the noise levels as well as the texture parameters and the granularity coefficient. The capability to estimate the latter is an important feature of the paper. The estimates are designed in an optimal manner as a risk minimizer that yields the marginal posterior maximizer for the labels and the posterior mean for the rest of the unknowns. They are computed based on a convergent procedure from samples of the posterior obtained through an advanced MCMC algorithm: Perturbation-Optimization step and Fisher Metropolis-Hastings step within a Gibbs loop. Various numerical evaluations provide encouraging results despite the strong difficulty of the problem.

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Unsupervised segmentation based on Von Mises circular distributions for orientation estimation in textured images

Cited by 2 publications

References 22 publications

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm

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