Binocular Vision Measurement Method Using Improved FAST and BRIEF

The detection using traditional features from accelerated segment test ( FAST) algorithm showed the existence of clustering corner phenomenon and the threshold value depended on artificial determination. Further, the detection using image matching algorithm showed that the matching and binocular vision measurement accuracies are low. In this paper, we proposed a binocular vision measurement method using improved FAST and binary robust independent elementary features (BRIEF). First, the FAST algorithm was used to extract the feature points and simplify the detection template. Next, the adaptive threshold was used to extract the feature points, which are described using the improved BRIEF, and the descriptor was formed by comparing the gray average of the neighborhood of a pixel. Then, it was based on the Hamming distance to complete the match. Finally, we adopted the gray gradient method to obtain the subpixel coordinates of the matching points. The three-dimensional spatial coordinates of the matching points were calculated using the principle of parallax and triangulation, to complete the size measurement of the measured object. From the experimental results, the corner points detected using the improved FAST are more uniform with regard to corner detection, verifying that the proposed method effectively improves the matching accuracy compared with other algorithms. Besides, the minimum relative error of measurement of proposed method is 0. 45%, which satisfies the measurement requirements.