Contour grouping with strong prior models

Abstract

Conventional approaches to perceptual grouping assume little specific knowledge about the object(s) of interest. However, there are many applications in which such knowledge is available and useful. We address the problem of finding the bounding contour of an object in an image when some prior knowledge about the object is available. We introduce a framework for combining prior probabilistic knowledge of the appearance of the object with probabilistic models for contour grouping. While prior probabilistic approaches have employed shortest-path algorithms to compute contours, this approach is limited in that many global properties cannot easily be incorporated in the computation. We propose as an alternative an approximate, constructive search technique, which finds a good (not necessarily optimal) solution, and which can accommodate important global cues and constraints. We apply this approach to the problem of computing exact lake boundaries from satellite imagery, given approximate prior models from an existing digital database. Our algorithm improves the accuracy of the prior GIS lake models by an average of 41%.