Journal article
Classification of rotated and scaled textured images using Gaussian Markov random field models
IEEE transactions on pattern analysis and machine intelligence, v 13(2), pp 192-201
01 Jan 1991
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This correspondence concerns the problem of classifying a test textured image that is obtained from one of C possible parent texture classes, after possibly applying unknown rotation and scale changes to the parent texture. The training texture images (parent classes) are modeled by Gaussian Markov random fields (GMRF's). A modified Bayes decision rule is used to classify a given test image into one of C possible texture classes. The classification power of the method is demonstrated through experimental results on natural textures from the Brodatz album.
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Details
- Title
- Classification of rotated and scaled textured images using Gaussian Markov random field models
- Creators
- F Cohen - Drexel UniversityZhigang FanM Patel
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 13(2), pp 192-201
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:A1991EY69900008
- Scopus ID
- 2-s2.0-0026103931
- Other Identifier
- 991019173954004721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic