The detection and feature extraction method of curvilinear convex regions with weak contrast using a gradient vector distribution model

In this work, we propose a new filter to detect and enhance lines in real images with varying contrast. It is robust against noise disturbances and also its output does not depend on the contrast. We first define the line-convergence vector field model based on the distribution of gradient vector orientation. Next we define a criterion index called the line-convergence degree to evaluate the likelihood of the existence of a line. The output of the proposed filter is defined as the average of line-convergence degrees in a region which is adapted to the gradient vector distribution. The filter output is a function of only gradient vector orientation and it is free from absolute intensity and relative contrast variations. Experimental results using artificial images and real images show the effectiveness of the proposed filter.