Research Article
Compare Effective Fuzzy Associative Memories for Grey-Scale Image Recognition
@INPROCEEDINGS{10.1007/978-3-642-36642-0_26, author={Pham Binh and Nong Hoa}, title={Compare Effective Fuzzy Associative Memories for Grey-Scale Image Recognition}, proceedings={Context-Aware Systems and Applications. First International Conference, ICCASA 2012, Ho Chi Minh City, Vietnam, November 26-27, 2012, Revised Selected Papers}, proceedings_a={ICCASA}, year={2013}, month={2}, keywords={Fuzzy Associative Memory Pattern Recognition Associative Memory}, doi={10.1007/978-3-642-36642-0_26} }- Pham Binh
Nong Hoa
Year: 2013
Compare Effective Fuzzy Associative Memories for Grey-Scale Image Recognition
ICCASA
Springer
DOI: 10.1007/978-3-642-36642-0_26
Abstract
Pattern recognition (PR) is the most important field of image processing that is widely developed by many scientists. Reason is that PR provides complete information of objects from noisy inputs. Several types of approach have been proposed to solve this problem, such as recognition base on key features, recognition base on the distribution of histogram. In studies about recognition using key features, Fuzzy Associative Memory (FAM) is an artificial neural network that solve effectively for PR. Advantages of FAM consist of compressing data and recalling from noisy inputs (noise tolerance). Therefore, FAM stores many patterns and retrieves stored patterns. In this paper, we present designs of effective FAM models and experiments to compare the ability of recall with nine types of noise. From results of experiments, we propose useful comments to choose an effective FAM model for pattern recognition applications.