A Contour Detection Method Based on Hierarchical Structure Response Model in Primary Visual Pathway

Based on the hierarchical response and dynamic transmission of the visual path structure, this paper proposes a new method of image contour detection. Aiming at the dark vision characteristics of retinal photoreceptor cells, a brightness-adaptive dark field adjustment model was established, and the orientation selectivity of multiscale classical receptive fields was used to construct the detection path of advanced contours and global contours; the characteristics of lateral geniculate nucleus (LGN) cells were simulated to sparse the information, combined with the side inhibition of non-classical receptive fields to suppress strong background textures; in addition, a micro-motion integration mechanism is proposed in the LGN region to reduce redundant texture information, and then the information is transmitted through adaptive synapses; finally, the primary contour response is transmitted across the view area is fed forward to the V1 area and global contour correction is performed, it quickly integrates with the advanced contour response. The natural images in the RuG40 and BSDS500 image libraries are used as experimental data. The average optimal P indicators of the detection results and the reference contour map are 0.50 and 0.32, respectively. The results show that this method can more effectively distinguish contours from textured edges and highlight the contours of the subject. This paper uses the inner mechanism of optic nerve cells and the dynamic transmission process of neural information to realize the encoding and detection of image contour information. It also provides new ideas for studying the subsequent visual perception of advanced visual cortex. Copyright ©2022 Acta Automatica Sinica. All rights reserved.