Abstract
Two methods of mosaicking images were developed, one for the general case and one for the simple case of a shift between two following images. The general method was evaluated by merging three images into a mosaic. When executed manually, the success rate of the method was 60%. With human assistance in selecting an appropriate number of corners to base the correlation on, the success rate increased to 89%. The operation took four minutes on a Pentium II 450 MHz Linux workstation, with most of this time spent on finding the correspondences. For the simple method, the shift was determined correctly in 95% of the tests runs for less noisy images. For noisy images, the success rate of properly merging all images of a mosaic was 20%. To increase the success rate, a function that determines the noise level was added. If too much noise was detected, the image is discarded and another one taken. This way, the error rate dropped to 10%.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 62-68 |
| Number of pages | 7 |
| Journal | IEEE Robotics and Automation Magazine |
| Volume | 11 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2004 |
Bibliographical note
Funding Information:We would like to thank the anonymous reviewers for their valuable comments. This material is based on work supported by the National Science Foundation through awards CNS-0224363 and CNS-0324864, Microsoft Corporation, and the Defense Advanced Research Projects Agency, Microsystems Technology Office (Distributed Robotics), ARPA Order G155, Program Code 8H20, issued by DARPA/CMD under Contract MDA972-98-C-0008.
Keywords
- Miniature robots
- Mosaicking
- Omnidirectional camera systems
- Panoramic imaging