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13

CORR
2016
Springer

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Supervised Texture Segmentation: A Comparative Study

8 years 27 days ago

Supervised Texture Segmentation: A Comparative Study

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This paper aims to compare between four different types of feature extraction approaches in terms of texture segmentation. The feature extraction methods that were used for segmentation are Gabor filters (GF), Gaussian Markov random fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM). It was shown that the GF performed best in terms of quality of segmentation while the GLCM localises the texture boundaries better as compared to the other methods. Keywords- texture measures; supervided segmentation; Bayesian classification.

Omar S. Al-Kadi

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Added	31 Mar 2016
Updated	31 Mar 2016
Type	Journal
Year	2016
Where	CORR
Authors	Omar S. Al-Kadi

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