ISID : In-order scan and indexed diffusion segmentation algorithm for stereo vision

Abstract

Existing segmentation algorithms have irregular computing order, expensive sorting, or inefficient backtracking procedure which would reduce their processing speed. In this paper, an in-order scan and indexed diffusion (ISID) segmentation algorithm for stereo vision which is more regular and does not need sorting nor backtracking is proposed. The in-order scan plateau detection is the first step in ISID which detects whether pixels in a 3x3 sliding window belongs to the same region or not. Then the indexed upward diffusion assigns a label to an undetermined pixel using a label diffusion method. Simulation results show that with the introduced regularity and lower complexity, the proposed ISID algorithm reduces 54% and 36% of the execution time when compared with the immersion-based and toboggan-based watershed algorithm in average.