A New Method of Depth Measurement with Binocular Vision Based on SURF
Chuanxu Wang, Sun Ying-he · 2009
Binocular measurement system has a wide range of applications and becomes a hot research direction in recent years. Traditional feature extraction and matching methods limit its accuracy and robustness. In this paper, we propose a new method of binocular depth measurement based on speed up robust feature (SURF). Firstly, we extract SURF feature points from object original image as a standard feature database. Then, nearest neighbor method is used to find matching features in the right and left images. To eliminate error matching features, we propose to use Grubbs method to remove outliers of visual disparities. Finally, object depth is figured out by the binocular disparity. Experiments show its accuracy and robustness.