A multi-label front propagation approach for object segmentation

Hua Li, Abderrahim Elmoataz, Jalal Fadili, Su Ruan

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)600-603
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
StatePublished - 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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