Artificial Visual System for Orientation Detection

Abstract

The human visual system is one of the most important components of the nervous system, responsible for visual perception. The research on orientation detection, in which neurons of the visual cortex respond only to a line stimulus in a particular orientation, is an important driving force of computer vision and biological vision. However, the principle underlying orientation detection remains a mystery. In order to solve this mystery, we first propose a completely new mechanism that explains planar orientation detection in a quantitative manner. First, we assume that there are planar orientation-detective neurons which respond only to a particular planar orientation locally and that these neurons detect local planar orientation information based on nonlinear interactions that take place on the dendrites. Then, we propose an implementation of these local planar orientation-detective neurons based on their dendritic computations, use them to extract the local planar orientation information, and infer the global planar orientation information from the local planar orientation information. Furthermore, based on this mechanism, we propose an artificial visual system (AVS) for planar orientation detection and other visual information processing. In order to prove the effectiveness of our mechanism and the AVS, we conducted a series of experiments on rectangular images which included rectangles of various sizes, shapes and positions. Computer simulations show that the mechanism can perfectly perform planar orientation detection regardless of their sizes, shapes and positions in all experiments. Furthermore, we compared the performance of both AVS and a traditional convolution neural network (CNN) on planar orientation detection and found that AVS completely outperformed CNN in planar orientation detection in terms of identification accuracy, noise resistance, computation and learning cost, hardware implementation and reasonability.