Publication Details
Overview
 
 
YanFeng Shang, Rudi Deklerck, Edgard Nyssen, Xin Yang
 

Chapter in Book/ Report/ Conference proceeding

Abstract 

Active contours are among the most successful image segmentation techniques used in a variety of applications. Edge based object extraction is historically the first and therefore also the most widespread instance of the active contour model. The disadvantage of this approach is in general that the active contour may leak out of the ideal contour when the edges are weak. Region based object extracting models show more advantages than edge based models on weak edges. A common disadvantage of such models is that during the execution of the iterative algorithm, the active contour will oscillate with a high speed on strong edges, because of the large deviation of the features between neighboring pixels. This sometimes leads to imprecise segmentation results. Motivated by these problems, we studied an object extracting model, combining edge and region features, building further on the approach developed in [1], called the Region Competition based Active Contour model (RCAC).

Reference