From edges to linear features: A PCA and graph based approach

Linear features such as line segments and contour fragments are important cues for object detection and scene analysis. Least square based and Hough-like approaches are quite popular and powerful. However, least square approaches are sensitive to outliers, and are unable to handle the case where there is more than one underlying line segment; while Hough-like approaches do not work well when extracting fragments which are not very `straight´. The goal of this work is to extract linear features locally in natural scenes using PCA and graph analysis. The whole process has five stages: first, for each local area, we create a graph using edge points as vertexes and distance measurements as links. Then, some edge-point sets which may contain underlying line segments are obtained by finding connected components in the graph; second, PCA is employed to estimate the existence of linear feature in each point set, and to compute the principal axis orientation; third, the relation between any two vertexes in each set is recalculated according to the principal axis orientation, and a new graph which only contains several trees is generated; fourth, one linear feature is detected by finding a best chain in each tree according to some criteria; fifth, all the line segments in the image are refined and organized according to global information.