Title :
Textured Image Segmentation Based on Spatial Dependence using a Markov Random Field Model
Author :
Schwartz, William Robson ; Pedrini, Helio
Author_Institution :
Comput. Sci. Dept., Maryland Univ., College Park, MD, USA
Abstract :
Image segmentation is a primary step in many computer vision tasks. Although many segmentation methods have been proposed in the last decades, there is no generic method that can be applied in a great variety of images. This work presents a new image segmentation method using texture features extracted by wavelet transforms combined with spatial dependence modeled by a Markov random field (MRF). The method initially produces a coarse segmentation, which is refined through a relaxation method based on a new energy function. A set of textured images is used to demonstrate the effectiveness of the proposed method.
Keywords :
Markov processes; computer vision; feature extraction; image segmentation; image texture; random processes; wavelet transforms; Markov random field model; coarse segmentation; computer vision tasks; energy function; relaxation method; spatial dependence model; texture feature extraction; textured image segmentation method; wavelet transforms; Computer science; Data mining; Feature extraction; Image segmentation; Labeling; Markov random fields; Parameter estimation; Pixel; Relaxation methods; Wavelet transforms; Image segmentation; image analysis; image texture analysis; minimization methods;
Conference_Titel :
Image Processing, 2006 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
1-4244-0480-0
DOI :
10.1109/ICIP.2006.312772