A Junction Point Detection Algorithm Based on Azimuth Consensus

Aiming at the shortcomings of traditional corners,a novel junction point detection algorithm which can extract local and structural information is proposed.First,sub-pixel positions of edge points are obtained using non-maximal suppression operation carried out in the gradient magnitude image according to gradient directions.Second,noises and irrelevant edges are filtered out based on azimuth consensus,and the remaining edge points are grouped relied on their inclination angles.And then,sup-pixel location of a junction points is found by a refinement strategy applied to the initial integer location of the junction.Finally,the inclination angles of edges points are updated according to the refined location of the junction,and optimal inclination angles is computed.Experimental results show that the proposed algorithm has good accuracy both in positioning and in inclination angles of branch edges,and has satisfactory robustness to noise disturbance and contrast change.