Conference proceeding
Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models
Intelligent robots, v 449(1), pp 17-28
06 Feb 1984
Abstract
Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data is modelled as one of C non-causal 2-D Markovian Stochastic Processes. The algorithms are designed to operate in real time when implemented on new parallel computer architectures. A doubly stochastic representation is used in image modelling. Here, an auto-normal (Gaussian) process is used to model textures in visible light and infrared images, and an auto-binary field is used to model apriori information about local image geometry. Image segmentation is realized as true maximum likelihood estimation. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region. The second segmentation algorithm is a relaxation-type algorithm that arises naturally within the context of these non-causal Markovian Processes. It is a simple, true maximum likelihood estimator. The algorithms can be used separately or together. These issues and subtleties concerning the use of the Markovian processes are discussed.
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6 citations in Scopus
Details
- Title
- Real Time Textured-Image Segmentation Based On Noncausal Markovian Random Field Models
- Creators
- Fernand S Cohen - University of Rhode IslandDavid B Cooper - Brown University
- Publication Details
- Intelligent robots, v 449(1), pp 17-28
- Publisher
- SPIE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-0021196887
- Other Identifier
- 991020531851804721