Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

We present a novel non-parametric unsupervised segmentation algorithm based on Region Competition [21]; but implemented within a Level Sets framework [11]. The key novelty of the algorithm is that it can solve N ≥ 2 class segmentation problems using just one embedded surface; this is achieved by controlling the merging and splitting behaviour of the level sets according to a Minimum Description Length (MDL) [6, 14] cost function. This is in contrast to N class region-based Level Set segmentation methods to date which operate by evolving multiple coupled embedded surfaces in parallel [3, 13, 20]. Furthermore, it operates in an unsupervised manner; it is necessary neither to specify the value of N nor the class models a-priori. We argue that the Level Sets methodology provides a more convenient framework for the implementation of the Region Competition algorithm, which is conventionally implemented using region membership arrays due to the lack of a intrinsic curve representation. Finally, we generalise the Gaussian region model used in standard Region Competition to the non-parametric case. The region boundary motion and merge equations become simple expressions containing cross- entropy and entropy terms.

Type

Conference paper

Publication Date

02/12/2003

Volume

2

Pages

1267 - 1274