RGB Line Pattern-Based Stereo Vision Matching for Single-Shot 3-D Measurement

Abstract

Active stereo vision has been used for real-time measurement in both academic and industrial fields. Currently, it is still very challenging for active stereo vision to achieve the continuous and robust measurement of a dynamic object due to the lack of a robust pattern extraction and matching method. In this article, we introduce a novel red green blue (RGB) line pattern with coarse-to-fine features in which the intervals between the adjacent green lines are largest and the intervals between the adjacent red lines are smallest. The large interval represents the coarse feature of the pattern and the small interval represents the fine feature of the pattern. Accordingly, we propose a matched pixel difference modeling (MPDM) method to model the matching relationship between two camera views based on the designed pattern. The line pattern is extracted in the hue saturation value (HSV) color space by slope difference distribution (SDD)-based threshold selection. The coarse green lines in two camera views are matched according to the minimum distance principle after the centroid-based alignment. The matching relationship is then modeled based on the matched green lines by least squares (LS) method. With the matching model, the fine blue lines and the fine red lines are matched successively. After stereo matching, the 3-D point on the measured object is computed by the ray intersection method. Experimental results showed that the proposed method is robust in measuring the dynamic objects by single-shot.