Fusion of visual cues of intensity and texture in Markov random fields image segmentation
This study proposes an algorithm that fuses visual cues of intensity and texture in Markov random fields region growing texture image segmentation. The idea is to segment the image in a way that takes EdgeFlow edges into consideration, which provides a single framework for identifying objects boundaries based on texture and intensity descriptors. This is achieved by modifying the energy minimisation process, so that it would penalise merging regions that have EdgeFlow edges in the boundary between them. Experimental results confirm the hypothesis that the integration of edge information increases the precision of the segmentation by ensuring the conservation of the homogeneous objects contours during the region growing process.
IET COMPUTER VISION
(2013). Fusion of visual cues of intensity and texture in Markov random fields image segmentation. IET COMPUTER VISION, 6(6), 603-609.
Available at: http://aquila.usm.edu/fac_pubs/7703