Robust automatic selection of optimal views in multi-view free-form object recognition

Authors:
Farzin Mokhtarian;Sadegh Abbasi
Affiliations:
Centre for Vision Speech and Signal Processing, Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK;Centre for Vision Speech and Signal Processing, Department of Electronic & Electrical Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper addresses the issue of automatic selection of the best and the optimum number of representative views for each object in a database that can enable the accurate recognition of that object from any single arbitrary view of the object. The object boundary in each view is represented by its curvature scale space (CSS) image. The CSS representation has been selected for MPEG-7 standardisation as a contour shape descriptor. The paper also presents a novel method for fusion of results from combined shape descriptors. The utilisation of this method for multi-view three-dimensional (3-D) object recognition has been explored. The object boundary of each view is represented effectively using the CSS technique, moment invariants and Fourier descriptors. It has been shown that the results obtained from the fusion method are superior to the results obtained from any single technique. The method has been tested on a collection of free-form 3-D objects. Each object has been modelled using an optimal number of silhouette contours obtained from different viewpoints. This number varies 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.