Rosalina Abdul Salam
Universiti Sains Malaysia
School of Computer Science
Marcos Aurelio Rodrigues
University of Sheffield Hallam
School of Computing and Management Sciences
S1 1WB Sheffield
Keywords: . Shape outline, viewpoint dependent, multiple viewpoints, classification, recognition, three-dimensional.
The design of a general purpose artificial vision system capable of recognizing arbitrarily complex three-dimensional objects without human intervention is still a challenging task in computer vision. Computer vision research has tried to incorporate knowledge of how human vision works and use this knowledge to design robust recognition systems. Early vision system, that is the primary visual cortex is where the edge and bar detection happen. These knowledge on how human vision works can be use to design a robust recognition system. Experiments have been conducted by incorporating these knowledge. Firstly, the process of shape outline detection and secondly, the use of multiple viewpoints of object. Shape outline readings are put through a normalization and dimensionality reduction process using an eigenvector based method to produce a new set of readings. Through statistical analysis, these readings together with other key measures, namely peak measures and distance measures, a robust classification and recognition process is achieved. Tests show that the suggested methods are able to automatically recognize three-dimensional objects from multiple viewpoints. Finally, experiments also demonstrate the system invariance to rotation, translation, scale, reflection and to a small degree of distortion. Tests also show that the suggested methods are able to automatically recognize three-dimensional objects from multiple viewpoints without any extra information required during the whole process.