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Images of nano-structures are often noisy. On the other hand, in many settings there is quite a lot of model knowledge regarding the observed structures. This paper proposes a method for segmenting an image using a geometric model of the the observed structure. The resulting segmentation is guaranteed to be globally optimal, for an explicitly specified score function. This property provides a great deal of robustness to the algorithm. The algorithm presented explores a pre-defined space of segmentations using a branch-and-bound algorithm. It eliminates those parts of the space that are provably poor and explores in further detail the more promising parts of the space. An example of a segmentation that can be obtained in this way is a straight line segmentation of an image into 2 regions that minimizes the intensity variation within the regions. Results showing extraction of specific nano-structures are presented. A trivial variation on the algorithm can find a maximum a-posteriori probability estimate of the segmentation when there exists an a-priori distribution over the segmentations and the objective function is interpreted as the likelihood of the image given the segmentation.
Computer Vision and Pattern Recognition Workshop, 2003. CVPRW '03. Conference on (Volume:2 )
Date of Conference: 16-22 June 2003