A model-based approach to junction detection using radial energy

Abstract

A novel junction detector is presented that fits the neighborhood around a point to a junction model. The junction model segments the neighborhood into wedges by determining a set of radial edges. The radial edges are invariant to affine transforms, creating an affine invariant junction detector. The radial edges are evaluated based upon the pixels along the edge. The angle between the pixel gradient and the vector to the potential junction point forms the initial basis for the measurement. An initial set of radial edges is selected based upon identifying local maximums within a given arc distance. An energy function is applied to the resulting radial segmentation, and a greedy optimization routine is used to construct the minimal set of radial edges. To identify the final junctions, a second energy function is used that combines the components of the first energy function with the resulting change in standard deviation by separation into radial segments. The junctions with the most energy in their local neighborhoods are selected as potential junctions. The neighborhoods about the potential junctions are analyzed to determine if they represent a single line or multiple non-parallel lines. If the neighborhood represents multiple non-parallel lines, the point is classified as a junction point. The junction detector is tested on several images including both synthetic and real images. Highlights of radially segmented junction points are displayed for the real images.