A NEW APPROACH FOR FEATURE POINT CLASSIFICATION, AGGREGATION, AND DESCRIPTION

Abstract

In this paper, an edge point, line point, or curve point is called a feature point. A new approach to extract junction points and to describe feature points is proposed here. It accepts as input data a binary image resulting from a feature detector without thinning. In the binary image, each black point is first classified based on the number of lines passing through it and on a local property that the classes of its neighboring points are almost the same. Next, an aggregation method is presented to group those classified points into several segments. The orientation of each segment is kept either clockwise or counterclockwise. Conic curves are then used to describe these segments. Finally, junction points including corner points, cross points, branch points, and inflection points are located. It is worth mentioning that the proposed method does not use any thinning process and curvature information. The effectiveness of the approach is also verified by one illustrative example and two experimental results.