Speed up junction detector based on azimuth consensus by Harris corner

Compared with common corner, junction point includes much structural data of branch edges. Due to its complicated computation, it prevents the wide use in many computer vision applications. In this paper, we propose a fast and effective junction point detection method based on Harris detector and azimuth consensus. To accelerate the process of the junction detection, we adopt Harris detector to filter out most pixels of the flat region and choose candidate junction set. Comparisons are made with other known detectors including CPDA, JUDOCA, and Lian’s method. The experimental results show our method has high-accuracy in location and branch edges’ orientation and good robustness for noise and contrast impact; what’s more, its computation time is reduced at high speed. Especially for real world image sets, our method can be more than 8 times faster than Lian’s and 1.58 times faster than JUDOCA.