Abstract
When using a monocular camera for detection or observation, one only obtain two-dimensional information, which is far from adequate for surgical robot manipulation and workpiece detection. Therefore, at this scale, obtaining three-dimensional information of the observed object, especially the depth information estimation of the surface points of each object, has become a key issue. This paper proposes two methods to solve the problem of depth estimation of defiant images in microscopic scenes. These are the depth estimation method of the defocused image based on a Markov random field, and the method based on geometric constraints. According to the real aperture imaging principle, the geometric constraints on the relative defocus parameters of the point spread function are derived, which improves the traditional iterative method and improves the algorithm’s efficiency.
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 callDisclaimer: 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.
