NDLI: Stochastic models in image analysis: parameter estimations and case studies in image segmentation

Content Provider	IEEE Xplore Digital Library
Author	El-Baz, A. Farag, A.A.
Copyright Year	2003
Description	Author affiliation: Comput. Vision & Image Process. Laboratory, Louisville Univ., KY, USA (El-Baz, A.; Farag, A.A.)
Abstract	Stochastic models of images are commonly represented in terms of three random processes (random fields) defined on the region of support of the image. The observed image process G is considered as a composite of two random process: a high level process X, which represents the regions (or classes) that form the observed image; and a low level process Y, which describes the statistical characteristics of each region (or class). The representation G = (X, Y) has been widely used in the image processing literature in the past two decades. In this paper we show how to use expectation maximization (EM) algorithm to get accurate model for the low level image by using mixture of normal distribution. The main idea of the proposed algorithm is as follow: first, we will use the EM algorithm to get the most dominance mixtures in the given density (empirical density), and then we assume that the absolute error between the empirical density and the estimated density is another density and we use the EM algorithm to estimate the number of mixtures in this error and the parameters for each mixtures. Then the estimated density for the absolute error is added or subtracted from the estimated density according to the sign (error). Convergence to the true distribution is tested using the Levy distance. A popular model for the high level process X has been the Gibbs-Markov random field (GMRF) model. In this paper we will use the same approach, which described in A. El-Baz et al. (July 2003) to estimate the parameters of GMRF. The approach has been applied on real images (spiral CT slices) and provides satisfactory results.
Sponsorship	IEEE Signal Processing Soc
Starting Page	154
Ending Page	157
File Size	273550
Page Count	4
File Format	PDF
ISBN	0780379977
DOI	10.1109/SSP.2003.1289367
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Image analysis Parameter estimation Image segmentation Random processes Image processing Gaussian distribution Convergence Testing Spirals
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Image segmentation using GMRF models: parameters estimation and applications

Polarimetric SAR image segmentation with hidden Markov random field models

The simplified method of finding values of width parameter kernel's estimations

SAR Image Segmentation Based on Markov Random Field Model and Multiscale Technology

Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation

Use of the mean-field approximation in an EM-based approach to unsupervised stochastic model-based image segmentation

Stochastic motion estimation and its applications

Gibbs random sequences Bayes estimation based on stochastic relaxation

A new look at finite mixture models in medical image analysis

Stochastic models in image analysis: parameter estimations and case studies in image segmentation

Similar Documents

Image segmentation using GMRF models: parameters estimation and applications

Polarimetric SAR image segmentation with hidden Markov random field models

The simplified method of finding values of width parameter kernel's estimations

SAR Image Segmentation Based on Markov Random Field Model and Multiscale Technology

Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation

Use of the mean-field approximation in an EM-based approach to unsupervised stochastic model-based image segmentation

Stochastic motion estimation and its applications

Gibbs random sequences Bayes estimation based on stochastic relaxation

A new look at finite mixture models in medical image analysis

Stochastic models in image analysis: parameter estimations and case studies in image segmentation