Fast object recognition using salient line groups

Dong Jung Kang, In So Kweon

Abstract: This paper presents an effective recognition method based on perceptual organization of low level features detected in an image. The method uses a dynamic programming (DP) based formulation to represent various line groups such as convex, concave, and more complex patterns consisting of convex and concave shapes. The essential features of perceptual organization such as endpoint proximity, collinearity, parallelism, and connectivity of lines, are incorporated into the DP based formulation as energy terms. As endpoint proximity, we detect two line junctions from image lines. We then search for junction groups by using collinearity constraint between the junctions. A DP-based search algorithm is used to detect a junction chain similar to the model chain, based on a local comparison. The proposed system is able to find line groups from images with broken lines and strong background clutters. We demonstrate the feasibility of our DP-based matching method based on perceptual organization using real images.