Image Segmentation Using Hough Transform

Abstract

Image segmentation, a way of extracting and representing information from an image is to group pixels together into regions of similarity. We would group pixels together according to the rate of change of their intensity over a region or the rate of change of depth in the image, corresponding to pixels lying on the same surface such as a plane, cylinder, sphere etc.
Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. In this paper we point out that the use of angle- radius rather than s lope-intercept parameters simplifies the computation further. We also show how the method can be used for circle detection.
The objective of this application is the recognition of different shapes in an image. This task can be subdivided into following procedures. First, an image is converted into gray scale. For detecting lines in images, we used histogram for the intensity related information and threshold. Then, an edge recognition procedure is implemented. We use the first-derivative Sobel detector to determine the edges and edge directions. Then, a Hough transform is accomplished on the threshold edge map for linking the edges. This is the procedure to detect straight line. For circle detection we wrote a function called “houghcircle”.