Depth from relative normal flows
dc.contributor.author | Jiang, Fan | en_US |
dc.contributor.author | Weymouth, Terry E. | en_US |
dc.date.accessioned | 2006-04-10T13:58:23Z | |
dc.date.available | 2006-04-10T13:58:23Z | |
dc.date.issued | 1990 | en_US |
dc.identifier.citation | Jiang, Fan, Weymouth, Terry E. (1990)."Depth from relative normal flows." Pattern Recognition 23(9): 1011-1022. <http://hdl.handle.net/2027.42/28932> | en_US |
dc.identifier.uri | http://www.sciencedirect.com/science/article/B6V14-48MPM3D-10V/2/fec7565c0b3188848b182a5fcf452554 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/28932 | |
dc.description.abstract | Most of the depth from image flow algorithms has to rely on either good initial guesses, or some assumptions about the object surfaces to achieve solutions that agree with the physical world. Waxman and Sinha point out that those restrictions can be relaxed if depth is computed from a relative image flow field. Since image flow determination is relatively much more difficult than normal flow determination, it is of interest to develop an algorithm to recover depth from normal flows. In this paper, we have shown that similar results can be obtained from relative normal flow fields as from relative image flow fields. We have implemented a normal flow estimation algorithm, and applied our algorithm to recover depth from intensity images. | en_US |
dc.format.extent | 1009470 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Depth from relative normal flows | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Science (General) | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/28932/1/0000769.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/0031-3203(90)90109-X | en_US |
dc.identifier.source | Pattern Recognition | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.