Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for...

The Hough Transform is the choice technique for identifying straight lines through digital images, with applications to high energy physics and computer vision. Classical methods for implementing the Hough transform of a N N binary image require to compute N3 additions over n = log2(N) bits integers, hence nN3 bit operations per transform. We introduce a new algorithm for computing the fast Hou...

A fast algorithm for the generalized Hough transform (GHT) based on the use of a hierarchical processing scheme and the inverse generalized Hough operation is proposed. By reducing the size of the image portion which need be processed in the proposed fast GHT, not only the computation time but also the number of processing elements for parallel processing can be reduced. The way to apply the pr...

This paper proposes a new method for recognition of geometrical shapes (such as lines, circles or ellipsoids) in an image. The main idea is to transform the problem into a bounded error estimation problem and then to use an interval-based method which is robust with respect to outliers. The approach is illustrated on an image taken by an underwater robot where a spheric buoy has to be detected....

The Generalized Hough Transform is a technique used to detect arbitrary objects in a given image. This technique is known for its capacity of absorption of distortions as well as noises. In the present paper, we describe an approach showing the efficiency of the use of the Generalized Hough Transform to recognize Arabic printed characters in their different shapes.

This paper describes the detection of faces in complex backgrounds where their sizes, positions and directions are arbitrary. We detect the faces by extracting face components such as eyes, a mouth and so on. We first extract face features and then calculate their likelihoods as each face component. Second we detect the face features which satisfy geometrical relations of the face. In order to ...