Image Contour Detection Based on Visual Pathway Information Transfer Mechanism

Abstract

Based on the coding mechanism and interactive features of visual information in the visual pathway, a new method of image contour detection is proposed. Firstly, simulating the visual adaptation characteristics of retinal ganglion cells, an adaptation & sensitization regulation model (ASR) based on the adaptation-sensitization characteristics is proposed, which introduces a sinusoidal function curve modulated by amplitude, frequency and initial phase to dynamically adjusted color channel response information and enhance the response of color edges. Secondly, the color antagonism characteristic is introduced to process the color edge responses, and the obtained primary contour responses is fed forward to the dorsal pathway across regions. Then, the coding characteristics of the “angle” information in the V2 region are simulated, and a double receptive fields model (DRFM) is constructed to compensate for the missing detailed contours in the generation of primary contour responses. Finally, a new double stream information fusion model (DSIF) is proposed, which simulates the dorsal overall contour information flow by the across-region response weighted fusion mechanism, and introduces the multi-directional fretting to simulate the fine-tuning characteristics of ventral detail features simultaneously, extracting the significant contours by weighted fusion of dorsal and ventral information streams. In this paper, the natural images in BSDS500 and NYUD datasets are used as experimental data, and the average optimal F-score of the proposed method is 0.72 and 0.69, respectively. The results show that the proposed method has better results in texture suppression and significant contour extraction than the comparison method.