Contour extraction by local parallel and stochastic algorithm which has energy learning faculty

Abstract

When a combined Markov random field (MRF) image model is applied to the contour extraction problem, the values of parameters specifying the model cannot be known. However, the parameter values can be learned from image examples containing a contour line. This paper first describes details of the learning algorithm. States of line process representing contour line obeys a probability distribution function determined by three aspects: input image, energy function and energy parameters called potential. In the learning algorithm presented here, a value of local potential which specifies an MRF image model is learned using the maximum likelihood method from a contour line image given as a teacher. By applying the proposed learning algorithm to real images, energy parameters are obtained that enable contour extraction that is superior in connectivity and has little noise. Moreover, as a consideration of the generalization faculty of the energy learning algorithm, energy learning for images and extracted contours of images (which are not good for learning) are performed using learned parameters. It was verified that contour lines extracted nonlearning images that are of the same quality as that of contour lines of learning images.