Inferring Homogeneous Regions from Rich Image Attributes
Olof Henricsson · Birkhäuser Basel eBooks · 1995
Image segmentation is an important part in any computer vision framework. However, the transition from local low-level representations to useful structures and relations in the intermediate levels has turned out to be a truly difficult problem. This paper addresses the difficult transition from low-level into intermediate-level vision, where the latter deals with producing a description of image and scene attributes in which more global relations are made explicit. We propose to combine a rich attributed contour representation with very general geometric contour relations. The implemented geometric relations, which are proximity, curvilinearity, parallelism and corner-like relations, allow to handle general man-made objects whose projected surfaces can be described by combinations of the defined relations. The combination of rich image attributes and geometric relations allows to discriminate between strong and weak contour relations. Strong relations require that not only the geometrical constraints are met but also that the contour attributes (e.g. photometric) are in agreement. We describe the approach and show some preliminary results. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.