Matching shape descriptions of objects

A model of an object is an image consisting of features of die object. The input is a gray scale image from which features are computed. In his doctoral thesis [7] J. L. Chen used a model based approach for object recognition. His method is based on Rosin's work [26] for extraction of parts. Both model and scene features are contour based properties. Properties of each part such as area, compactness, convexity etc. are computed and used to match the scene image to the model. This paper extends the algorithm in several directions. The contours are improved using two passes over the initial input image. The notion of internal part or base of an object is introduced and used to normalize the part areas. Insignificant parts are merged with neighboring parts to provide a better segmentation of the scene. Interpretation trees are used to match scene to object. The algorithm is tested on simple hand drawn images and also images of buildings obtained from architectural databases.