Open AccessJournal Article10.4208/CICP.160609.311209A

Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation

- 01 Jun 2010

- Communications in Computational Physics

- Vol. 8, Iss: 3, pp 623-641

TL;DR: This paper proposes a multi-phase fuzzy region competition model for texture image segmentation and shows that the proposed method is competitive with the other state-of-the-art segmentation methods.

Abstract: In this paper, we propose a multi-phase fuzzy region competition model for texture image segmentation. In the functional, each region is represented by a fuzzy membership function and a probability density function that is estimated by a nonparametric kernel density estimation. The overall algorithm is very e‐cient as both the fuzzy membership function and the probability density function can be implemented easily. We apply the proposed method to synthetic and natural texture images, and synthetic aperture radar images. Our experimental results have shown that the proposed method is competitive with the other state-of-the-art segmentation methods.

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In this paper, the authors propose a multi-phase fuzzy region competition model for texture image segmentation.. The authors apply the proposed method to synthetic and natural texture images, and synthetic aperture radar images.

Figure 1: Two-phase segmentation. The First and Fourth columns: synthetic images; the Second and Fifth columns: segmentation results; the Third and Sixth columns: membership functions u1.

Figure 7: Evolution of the membership functions with different initializations on a zebra image. The final results from top to bottom are obtained at iterations 105, 270, 150 and 240 respectively.

Figure 8: Two-phase SAR image segmentation. The First column: SAR images; the Second column: segmentation results; the Third column: membership functions u1; the Fourth column: segmentation results of the other methods: (d) by the method in [15]; (h) and (l) by the method in [16]; (p) by the method in [12].

Figure 3: Three-phase image segmentation. The First column: synthetic images; the Second column: segmentation results; the Third column: membership functions u1; the Fourth column: membership functions u2.

Figure 9: Three-phase SAR image segmentation. (a) SAR image; (b) segmentation result by the proposed method; (c) membership function u1; (d) membership function u2; (e) segmentation result by method in [29]; (f) segmentation result by method in [12].

Figure 4: Four-phase image segmentation. (a) synthetic image; (b) segmentation result; (c) membership function u1; (d) membership function u2; (d) membership function u3.

Citations

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Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model

Shoulin Yin, +2 more

- 10 May 2018

- IEEE Access

TL;DR: A new large-scale remote sensing image segmentation method that combines fuzzy region competition and the Gaussian mixture model is presented that has the feasibility and effectiveness and can achieve highly accurate segmentation results compared with current state-of-the-art methods.

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A comprehensive texture segmentation framework for segmentation of capillary non-perfusion regions in fundus fluorescein angiograms.

Yalin Zheng, +7 more

- 18 Apr 2014

- PLOS ONE

TL;DR: A comprehensive segmentation framework that can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications is proposed.

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Journal Article•10.1109/TIP.2011.2182522

A Direct Approach Toward Global Minimization for Multiphase Labeling and Segmentation Problems

Ying Gu, +2 more

- 01 May 2012

- IEEE Transactions on Image Processing

TL;DR: A new primal-dual approach for global minimization of the continuous Potts model with applications to the piecewise constant Mumford-Shah model for multiphase image segmentation is proposed.

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MR image segmentation and bias field estimation based on coherent local intensity clustering with total variation regularization

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- 04 Jul 2016

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TL;DR: A new unified MR image segmentation algorithm whereby tissue segmentation, bias correction and noise reduction are integrated within the same energy model, presented by a total variation term introduced to the coherent local intensity clustering criterion function.

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Wavelet-based fuzzy multiphase image segmentation method

Yafeng Li

- 01 Feb 2015

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TL;DR: The proposed wavelet-based fuzzy multiphase image segmentation model introduces wavelet based regularization on the membership functions which are used as indicators of different regions to efficiently solve the model.

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References

Journal Article•10.1016/0167-2789(92)90242-F

Nonlinear total variation based noise removal algorithms

Leonid I. Rudin, +2 more

- 01 Nov 1992

- Physica D: Nonlinear Phenomena

TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.

...read moreread less

17.3K

•Journal Article•10.1109/83.902291

Active contours without edges

Tony F. Chan, +1 more

- 01 Feb 2001

- IEEE Transactions on Image Processing

TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.

...read moreread less

11.6K

•Journal Article•10.1214/AOMS/1177704472

On Estimation of a Probability Density Function and Mode

Emanuel Parzen

- 01 Sep 1962

- Annals of Mathematical Statistics

TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.

...read moreread less

11.4K

•Book

Measure theory and fine properties of functions

LawrenceCraig Evans

- 01 Jan 1992

TL;DR: In this article, the authors define and define elementary properties of BV functions, including the following: Sobolev Inequalities Compactness Capacity Quasicontinuity Precise Representations of Soboleve Functions Differentiability on Lines BV Function Differentiability and Structure Theorem Approximation and Compactness Traces Extensions Coarea Formula for BV Functions isoperimetric inequalities The Reduced Boundary The Measure Theoretic Boundary Gauss-Green Theorem Pointwise Properties this article.

...read moreread less

6.7K

Journal Article•10.1109/ICCV.1995.466871

Geodesic active contours

Vicent Caselles, +2 more

- 20 Jun 1995

TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.

...read moreread less

6.1K

...

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Kernel Density Estimation Based Multiphase Fuzzy Region Competition Method for Texture Image Segmentation

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1. What are the contributions mentioned in the paper "Kernel density estimation based multiphase fuzzy region competition method for texture image segmentation" ?

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Citations

Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model

A comprehensive texture segmentation framework for segmentation of capillary non-perfusion regions in fundus fluorescein angiograms.

A Direct Approach Toward Global Minimization for Multiphase Labeling and Segmentation Problems

MR image segmentation and bias field estimation based on coherent local intensity clustering with total variation regularization

Wavelet-based fuzzy multiphase image segmentation method

References

Nonlinear total variation based noise removal algorithms

Active contours without edges

On Estimation of a Probability Density Function and Mode

Measure theory and fine properties of functions

Geodesic active contours

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