Title :
Supervised texture segmentation: A comparative study
Author :
Al-Kadi, Omar S.
Author_Institution :
King Abdullah II Sch. for IT, Univ. of Jordan, Amman, Jordan
Abstract :
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 :
Gabor filters; Markov processes; feature extraction; image segmentation; image texture; matrix algebra; GF; GMRF; Gabor filters; Gaussian Markov random fields; RLM; comparative study; feature extraction; run length matrix; supervised texture segmentation; Bayesian classification; supervided segmentation; texture measures;
Conference_Titel :
Applied Electrical Engineering and Computing Technologies (AEECT), 2011 IEEE Jordan Conference on
Conference_Location :
Amman
Print_ISBN :
978-1-4577-1083-4
DOI :
10.1109/AEECT.2011.6132529
