Invariant Object Recognition Using a Distributed Associative Memory
Abstract
This paper describes an approach to 2-dimensional object recognition. Complex-log con(cid:173) formal 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 ver(cid:173) sion 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, us(cid:173) ing real, gray scale images, are presented to show the feasibility of our approach.
Cite
Text
Wechsler and Zimmerman. "Invariant Object Recognition Using a Distributed Associative Memory." Neural Information Processing Systems, 1987.Markdown
[Wechsler and Zimmerman. "Invariant Object Recognition Using a Distributed Associative Memory." Neural Information Processing Systems, 1987.](https://mlanthology.org/neurips/1987/wechsler1987neurips-invariant/)BibTeX
@inproceedings{wechsler1987neurips-invariant,
title = {{Invariant Object Recognition Using a Distributed Associative Memory}},
author = {Wechsler, Harry and Zimmerman, George Lee},
booktitle = {Neural Information Processing Systems},
year = {1987},
pages = {830-839},
url = {https://mlanthology.org/neurips/1987/wechsler1987neurips-invariant/}
}