Automatic Selection of Optimal Views in Multi-view Object Recognition

A shape-based method for multi-view 3-D object representation and recognition is introduced and explored in this paper. 3-D objects are recognised by a small number of images taken from different views. The paper addresses the issue of automatic selection of the best and the optimum number of views for each object. The object boundary of each view is considered as a 2-D shape and is represented effectively by less than ten pairs of integer values. These values include the locations of the maxima of its Curvature Scale Space (CSS) image contours. The CSS shape descriptor is expected to be selected for MPEG-7 standardisation. An unknown object is then recognised by a single image taken from an arbitrary viewpoint. The method has been tested on a collection of 3-D objects consisting of 15 aircrafts of different shapes. Each object has been modelled using an optimised number of silhouette contours obtained from different view points. This number varies from 5 to 25 depending on the complexity of the object and the measure of expected accuracy. A comprehensive analysis of the performance of the system has been given in this paper as the number of views varies. Around ten silhouette contours corresponding to random views are separately used as input for each object. Results indicated that robust and efficient 3-D free-form object recognition through multi-view representation can be achieved using the CSS representation even for large database retrieval applications.