MODEL-BASED OBJECT RECOGNITION USING PROBABILISTIC LOGIC AND MAXIMUM ENTROPY
Thomas B. Kane, Patrick McAndrew, Andrew Michael Wallace · International Journal of Pattern Recognition and Artificial Intelligence · 1991
In the visual context, a reasoning system should he capable of inferring a scene description using evidence derived from data-driven processing of the iconic image data. This evidence may consist of a set of curvilinear boundaries, which are obtained by grouping local edge data into extended features. Using linear primitives, a framework is described which represents the information contained in pre-formed models of possible objects in the scene, and in the segmented scenes themselves. A method based on maximum entropy is developed which assigns measures of likelihood for the presence of objects in the two-dimensional image. This method is applied to and evaluated on real and simulated image data, and the effectiveness of the approach is discussed.