Angle Histogram of Hough Transform as Shape Signature for Visual Object Classification (AHOC).

Abstract

This work presents a new method for object classification using Hough transform (HT) and angle histogram as an object signature. Several methods are reported in the literature that exploit HT and other techniques as a pre-processing step to characterise objects to be used in detection, recognition, classification, etc. HT is a powerful technique to extract shape features from 2D objects; it has been used in many studies and implemented successfully in many applications. Our study is unique by post processing HT voting space using a binary threshold then computing an angle histogram of the resulting angle space as a shape signature of objects. Our image set consisted of 25 simple geometric shapes and six complex natural object classes of: trees, people, cars, airplanes, houses and horses. The method was trained and tested using 225 images from six different classes and found to be robust with a classification accuracy of 95.83%.