Generalized Hough Transform for object classification in the maritime domain

Abstract

A Generalized Hough Transform (GHT)-based classification scheme for an object-of-interest in maritime-domain images is proposed in this thesis. First, the object edge points are extracted and used to generate a representation of the object as a Hough coordinate table by using the GHT algorithm. The table is then reformatted to a contour map called a Hough features map. The coordinates of dominant peaks or Hough features on the map are extracted and fed into a feed-forward, back-propagation neural network for classification. In this research, the scheme is tested using perfect shapes of triangles, squares, circles, and stars and maritime-domain images of ships, aircraft, and clouds, and the classification results obtained are reported.