Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F09%3A00327903" target="_blank" >RIV/67985556:_____/09:00327903 - isvavai.cz</a>

Alternative codes found

RIV/61384399:31160/09:00033501

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

Original language description

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions.Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BD - Information theory

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Image Processing

ISSN

1057-7149

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

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UT code for WoS article

000268033300012

EID of the result in the Scopus database

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