Edge grouping based on Gestalt principles and spectral clustering
Xiao Yu Sun, Hao Dou, Ke Shang, Jinwen Tian, Delie Ming, Xiangchun Li · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
The human visual systems tend to integrate oriented line segments into groups if they follow the Gestalt principles. It is commonly acknowledged that early human visual processing operates bye first performing edge detection followed by perceptual organization to group edges into object-like structures. Edge groups can be used to improve a variety of tasks such as multi-threshold selection, object proposal generation sketch segmentation. In this paper, a perceptual grouping framework that organizes image edges into meaningful structures is proposed. The grouper formulates edge grouping as a spectral clustering problem, where a computation model based on Gestalt principles is developed to encode probabilities of candidate edge pairs. First, a probability model is proposed as grouping constraint inspired by the Gestalt principles, i.e. proximity, continuity and similarity. Then we take the grouping constraint as the input and perform spectral clustering to integrate edge fragments into groups. Experiments have shown that our algorithm can effectively organizes image edges into meaningful structures.