Avtor/Urednik		Kervrann, Charles
Naslov		Bayesian image segmentation through level lines selection
Tip		članek
Vir		Image Anal Stereol
Vol. in št.		Letnik 20, št. Suppl 1
Leto izdaje		2001
Obseg		str. 251-6
Jezik		eng
Abstrakt		Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image segmentation, the a priori distribution should capture the knowledge about objects. Taking inspiration from (Alvarez et al., 1999), we design a prior density that penalizes the area of homogeneous parts in images. The segmentation problem is further formulated as the estimation of the set of curves that maximizes the posterior distribution. In this paper, we explore a posterior distribution model for which its maximal mode is given by a subset of level curves, that is the boundaries of image level sets. For the completeness of the paper, we present a stepwise greedy algorithm for computing partitions with connected components.
Deskriptorji		BAYES THEOREM IMAGE PROCESSING, COMPUTER-ASSISTED