Brain-like contour detector following Retinex theory and Gestalt perception grouping principles

Abstract

Numerous contour detection algorithms draw inspiration from biological vision systems. These algorithms imitate the way simple cells extract edges using Gabor filters. They also suppress edges generated by image textures simulating the non-classical receptive fields (NCRFs), thereby popping up the object contours within the image edges. However, these algorithms are not flawless and may yield imperfect results due to noise pollution, unsatisfactory lighting, limitations in image processing algorithms, and likewise. Weak strengths and pixel loss in contour segments are two common issues. In this paper, we provide two strategies to address these challenges. First, we separate the illumination component from the image following Retinex theory, extract the illumination contour using bio-inspired filters, and boost contour strengths by superimposing the illumination contour. Second, we complete object contours by filling small gaps in contours, using a proposed linking likelihood function that is a joint probability of element distance and orientation difference, following Gestalt perceptual grouping principles. Although not performance-oriented, the experimental results show that our endeavors improve the performance of bio-inspired contour detectors. More importantly, we demonstrate the significance of visual computation theories such as the Retinex theory and the Gestalt perception grouping principle for contour detection.