Abstract
Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel implementations.
Highlights
Stereovision is an area of computer vision focusing on the extraction of 3D information from digital images
The most researched aspect of this field is stereo matching: given two or more images as input, matching pixels have to be found across all images so that their 2D positions can be converted into 3D depths, producing as result a 3D estimation of the scene
We present the data sets used for experiments, with the main characteristics, as well as the quality metrics used to assess the performance of the algorithms tested
Summary
Stereovision is an area of computer vision focusing on the extraction of 3D information from digital images. The most researched aspect of this field is stereo matching: given two or more images as input, matching pixels have to be found across all images so that their 2D positions can be converted into 3D depths, producing as result a 3D estimation of the scene. As many other breakthrough ideas in computer science, stereovision is strongly related to a biological concept, namely stereopsis, which is the impression of depth that is perceived when a scene is viewed by someone with both eyes and normal binocular vision [1]. The earliest stereo matching algorithms were developed in the field of photogrammetry for automatically constructing topographic elevation maps from overlapping aerial images [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
