Bayesian Approach to Perceptual Edge Preservation in Computer Vision

Abstract

This paper presents a novel approach for preserving perceptual edges representing boundaries of objects as perceived by human eyes. First, a subset of pixels (pixels of interest, POI) in an input image is selected by a pre-process of removing background and noise. One by one as a target pixel, each of POI is subjected to a Bayesian decision. This approach is characterized by iteratively employing a shape-variable mask to sample gradient orientations of pixels for measuring the directivity of a target pixel, the mask shape is updated after each iteration. We show that a converged mask covers pixels that best fit the orientation similarity with the target pixel, which in effect fulfills the similarity and proximity principles in Gestalt theory. Subsequently, a Bayesian rule is applied to the converged directivity to determine whether the target pixel belongs to a perceptual edge. Instead of using state-of-the-art edge detectors such as Canny detector [1], a pre-process combining Gaussian Mixture Model (GMM) [2] and Difference of Gaussian (DoG) [3] is devised to select POI, wherein GMM is responsible for removing the background of an input image (first screening), whereas DoG for filtering noisy or false contours (second screening). Experimental results indicate that a great amount of computational load can be saved, in comparison with the use of Canny detector in our previous work [4]. Since the perceptual edges are useful for forming a complete object contour corresponding to the human visual perception, the results of this paper can be potentially cooperated with other more advanced object detection methods such as the deep learning-based SSD [5] to achieve the same effect as the human visual system in dealing with obscured or corrupted input images, whereby even if a target object is occluded by other objects or corrupted by rainy water, it can be still identified correctly, this feature should greatly enhance the operational safety of unmanned vehicles, unmanned aircraft and other autonomous systems.