Journal article
Morphologically constrained GRFs: applications to texture synthesis and analysis
IEEE transactions on pattern analysis and machine intelligence, Vol.21(2), pp.99-113
02/1999
Handle:
https://hdl.handle.net/2376/113942
Abstract
A new class of Gibbs random fields (GRFs) is proposed capable of modeling geometrical constraints in images by means of mathematical morphology. The proposed approach, known as morphologically constrained GRFs, models images by means of their size density. Since the size density is a multiresolution statistical summary, morphologically constrained GRFs explicitly incorporate multiresolution information into image modeling. Important properties are studied and their implication to texture synthesis and analysis is discussed. Statistical inference can be easily implemented by means of mathematical morphology. This allows the design of a computationally simple morphological Bayes classifier which produces excellent results in classifying natural textures.
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Details
- Title
- Morphologically constrained GRFs: applications to texture synthesis and analysis
- Creators
- K Sivakumar - Sch. of Electr. Eng. & Comput. Eng., Washington State Univ., Pullman, WA, USAJ Goutsias
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, Vol.21(2), pp.99-113
- Academic Unit
- Voiland College of Engineering and Architecture
- Publisher
- IEEE
- Identifiers
- 99900547803501842
- Language
- English
- Resource Type
- Journal article