A Markov pixon information approach for low-level image description

Author

Descombes, Xavier ; Kruggel, Frithjof

Author_Institution

Image Process. Group, Max-Planck-Inst. of Cognitive Neurosci., Leipzig, Germany

Volume

21

Issue

6

fYear

1999

fDate

6/1/1999 12:00:00 AM

Firstpage

482

Lastpage

494

Abstract

The problem of extracting information from an image which corresponds to early stage processing in vision is addressed. We propose a new approach (the MPI approach) which simultaneously provides a restored image, a segmented image and a map which reflects the local scale for representing the information. Embedded in a Bayesian framework, this approach is based on an information prior, a pixon model and two Markovian priors. This model based approach is oriented to detect and analyze small parabolic patches in a noisy environment. The number of clusters and their parameters are not required for the segmentation process. The MPI approach is applied to the analysis of statistical parametric maps obtained from fMRI experiments

Keywords

Bayes methods; Markov processes; image restoration; image segmentation; minimum entropy methods; Bayesian framework; Markov pixon information approach; Markovian priors; early stage processing; information extraction; information prior; low-level image description; model based approach; parabolic patches; pixon model; statistical parametric maps; Bayesian methods; Data mining; Entropy; Image analysis; Image restoration; Image segmentation; Layout; Markov random fields; Spatial resolution; Working environment noise;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.771311

Filename

771311