Iterative relative fuzzy connectedness for multiple objects with multiple seeds

In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa et al. [J.K. Udupa, P.K. Saha, R.A. Lotufo, Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1485–1500] and Saha and Udupa [P.K. Saha, J.K. Udupa, Iterative relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation, in: Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, Hilton He...

• Type: Journal
• Year: 2007
• Where: CVIU
• Authors: Krzysztof Ciesielski, Jayaram K. Udupa, Punam K. Saha, Ying Zhuge
• Keywords: Algorithms, CVIU 2007, Image Segmentation, Relative Fuzzy Connectedness