Epipolar geometry and stereo matching algorithm for underwater fish-eye images

Abstract

A fish-eye lens achieves a large field of view at the cost of image distortion, and underwater imaging by a fish-eye lens introduces refraction when light passes through the different medium. To obtain three-dimensional information, under these circumstances, a stereo matching algorithm is proposed that analyzes the geometric characteristics of an underwater fish-eye image, taking into account distortion and refraction. First, the underwater imaging model of a stereo fisheye camera is established, and the epipolar curve of the underwater fish-eye image is calculated. Then, in the matching step, an adaptive window based on mean-shift segmentation is proposed to further alleviate the impact of distortion. Experiments are performed on synthetic images and natural scene images. The results show that the proposed model and the calculated epipolar curve are effective and that the adaptive window method could improve the precision of stereo matching on underwater fish-eye images.