NDLI: Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images

Content Provider	IEEE Xplore Digital Library
Author	Cooper, David B.
Copyright Year	1979
Abstract	Effective and elegant procedures have recently appeared in the published literature for determining by computer a highly variable blob boundary in a noisy image [1]-[3]. In this paper we point out that if the blob boundary is modeled as a Markov process and the additive noise is modeled as a white Gaussian noise field, then maximization of the joint likelihood of the hypothesized blob boundary and all of the image data results in roughly the same blob boundary determination as does one of the aforementioned deterministic formulations [2]. However, the formulation in this paper provides insights into and optimal parameter values for the functions involved and reveals suboptimalities in some of the formulations appearing in the literature. More generally, we agree that maximization of the joint likelihood of the hypothesized blob boundary and of the entire picture function is a fundamental approach to boundary estimation or the estimation of linear features (roads, rivers, etc.) in images, and provides a powerful mechanism for designing sequential, parallel, or other boundary estimation algorithms. The ripple filter, an advanced form of region growing, is briefly introduced and illustrates one of a number of alternative algorithms for maximizing the likelihood function. Hence, this likelihood maximization approach provides a unified view for seemingly different approaches, such as sequential boundary finding and region growing. Bounds on the accuracy of boundary estimation are readily derived with this formulation and are presented.
Sponsorship	IEEE Computer Society
Starting Page	372
Ending Page	384
Page Count	13
File Size	2998535
File Format	PDF
ISSN	01628828
Volume Number	PAMI-1
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1979-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Gaussian noise Markov processes Image recognition Additive noise Roads Rivers Algorithm design and analysis Filters Costs random fields Boundary estimation in images cost minimization error analysis image recognition likelihood maximization pattern recognition
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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

Maximum likelihood estimation of noisy Gaussian Markov random fields

Restoration of noisy regions modeled by noncausal Markov random fields of unknown parameters

Motion estimation in noisy ultrasound images by maximum likelihood

Bayesian restoration of noisy images with the EM algorithm

Recursive enhancement of noncausal images

Pattern Recognition in Image Processing Using Interpixel Correlation

Simultaneous optimal segmentation and model estimation of nonstationary noisy images

Maximum likelihood estimation methods for multispectral random field image models

Optimal target location in multichannel images

Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images

Similar Documents

Maximum likelihood estimation of noisy Gaussian Markov random fields

Restoration of noisy regions modeled by noncausal Markov random fields of unknown parameters

Motion estimation in noisy ultrasound images by maximum likelihood

Bayesian restoration of noisy images with the EM algorithm

Recursive enhancement of noncausal images

Pattern Recognition in Image Processing Using Interpixel Correlation

Simultaneous optimal segmentation and model estimation of nonstationary noisy images

Maximum likelihood estimation methods for multispectral random field image models

Optimal target location in multichannel images

Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images