A connectionist approach for peak detection in Hough space

摘要

Peak detection in multidimensional parameter space is an important aspect of feature detection using the Hough transform. In this paper a connectionist network is presented for detecting peaks in multidimensional Hough space. The neural network implementation successfully uses circumstantial evidence and detects multiple winners over the parameter space such that these winners correspond to parameters of features in the image. The dynamics of the network has been analysed and the conditions for convergence have been established. Experimental results obtained by applying the network for detecting straight lines in three images are presented. The results obtained are promising.