This thesis investigates the problem of estimating the three-dimensional structure of a scene from a sequence of images. Structure information is recovered from images continuously using shading, motion or other visual mechanisms. A Kalman filter represents structure in a dense depth map. With each new image, the filter first updates the current depth map by a minimum variance estimate that best fits the new image data and the previous estimate. Then the structure estimate is predicted for the next time step by a transformation that accounts for relative camera motion. Experimental evaluation shows the significant improvement in quality and computation time that can be achieved using this technique.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6808 |
| Date | 01 May 1991 |
| Creators | Heel, Joachim |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 149 p., 23730458 bytes, 8484961 bytes, application/postscript, application/pdf |
| Relation | AITR-1296 |
Page generated in 0.0023 seconds