This paper describes an approach to two-dimensional object recognition. Complex-log conformai mapping is combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real, gray scale images, are presented to show the feasibility of our approach.
|Original language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|State||Published - Nov 1988|
Bibliographical noteFunding Information:
Manuscript received November 5, 1986; revised October 27, 1987. Recommended for acceptance by C. Brown. This work was supported in part by the National Science Foundation under Grant ECS-83 10057 and by a grant from the Microelectronics and Information Science (MEIS) Center of the University of Minnesota.
- Complex-log mapping
- distributed associative memory
- pattern recognition
- space variant filtering