Line and circle finding by the weighted Mahalanobis distance transform and extended Kalman filtering

The paper presents a new parameter space approach, called the Weighted Mahalanobis Distance Hough Transform (WMDHT) whose main merit is to incorporate formal stochastic image and feature noise models. It is aimed at improving the efficiency, accuracy and reducing the size of the accumulator arrays by combining it with extended Kalman filter refinement. It works by detecting image feature points in the neighbourhood of a contour instead of exactly on the contour through a Mahalanobis distance measure modified by a weight function inversely proportional to the distance between the point and an ideal contour. The method is applicable to geometric features of any dimensionality and the paper illustrates it by considering detection of straight and circular segments