Abstract

This paper analyzes the extent to which a semiglobal algorithm for stereo vision is stable against the picture-taking conditions and the characteristics of the object of observation in the soft-approach problem. It is shown that the algorithm under investigation is stable against various types of background under different illumination conditions and in the presence of noise. For long distances, it is recommended that the internal parameters of the algorithm undergo fine-tuning by means of machine-training methods. A technique is developed that makes it possible to establish the stability of the algorithm against shapes of the object of observation that vary in complexity.

© 2015 Optical Society of America

Integration of stereo vision and optical flow by using an energy-minimization approach

Nasser M. Nasrabadi, Sandra P. Clifford, and Yi Liu
J. Opt. Soc. Am. A 6(6) 900-907 (1989)

Binocular stereo matching of real scenes based on a convolutional neural network and computer graphics

Liaoyu Kou, Kai Yang, Lin Luo, Yu Zhang, Jinlong Li, Yong Wang, and Liming Xie
Opt. Express 29(17) 26876-26893 (2021)

Sensor fusion of phase measuring profilometry and stereo vision for three-dimensional inspection of electronic components assembled on printed circuit boards

Deokhwa Hong, Hyunki Lee, Min Young Kim, Hyungsuck Cho, and Jeon Il Moon
Appl. Opt. 48(21) 4158-4169 (2009)

Single-shot 3D shape measurement using an end-to-end stereo matching network for speckle projection profilometry

Wei Yin, Yan Hu, Shijie Feng, Lei Huang, Qian Kemao, Qian Chen, and Chao Zuo
Opt. Express 29(9) 13388-13407 (2021)

Accurate 3D reconstruction of single-frame speckle-encoded textureless surfaces based on densely connected stereo matching network

Ruike Wang, Pei Zhou, and Jiangping Zhu
Opt. Express 31(9) 14048-14067 (2023)