Object identification using automated decision tree construction approach for robotics applications

Abstract

Abstract The objective of this article is to present a novel approach to object recognition and classification for robotic applications using the automated decision tree generation technique. The method developed relies upon simple statistical measurements extracted from object classes and represented in the form of a distance matrix ‘D’ to form a decision tree. The algorithms presented here are computationally efficient and simple to implement. The effectiveness of the features are automatically assessed, allowing for the automatic selection of only those features needed to accomplish object recognition and classification. The performance of the algorithms are successfully tested and demonstrated.