For effective image segmentation methods, speed, accuracy and smoothness of the result are essential. In this paper, an iterative object segmentation approach is proposed based on minimal path theory. Each iterative step includes one morphological dilatation and one multi-label front propagation. A narrow band is obtained by dilating the current contour with the known size. A new contour is again formed by multi-label front propagation, which is based on minimal path theory. Its propagation speed is decided by the local image mean values together with the edge function. The final boundary will be obtained automatically through finite iterations. This algorithm is a global optimization method. It is simple and fast with complexity O(N). The initial contour may be chosen freely. The multi-label front propagation guarantees continuity and smooth contours with the capability to handle topology changes. Furthermore, it is easy to extend to the 3D case. Some experimental results are also presented.
|Number of pages
|Proceedings - International Conference on Pattern Recognition
|Published - 2004
|Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004 → Aug 26 2004
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition