Title: Angle histogram of Hough transform as shape signature for visual object classification – (AHOC)

Authors: Aaron Rasheed Rababaah

Addresses: Department of Computer Science and Information Systems, American University of Kuwait (AUK), State of Kuwait

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%.

Keywords: visual object characterisation; object classification; Hough transform; angle histogram; template matching.

DOI: 10.1504/IJCVR.2020.108150

International Journal of Computational Vision and Robotics, 2020 Vol.10 No.4, pp.312 - 336

Accepted: 06 Jun 2019
Published online: 03 Jul 2020 *

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