Title
2-D Shape Recognition Using Recursive Landmark Determination And Fuzzy Art Network Learning
Keywords
2-D shape recognition; Fuzzy ART network; Recursive landmark determination
Abstract
In this Letter, 2-D shape recognition is done using a combination of recursive search of landmarks, landmark-based invariant features, and a fuzzy ART neural- network classifier. To make this novel combination work well, an upper limit is imposed on the number of total landmarks allowed, and this maximum size is then translated into fixed dimensions of invariant features and into the neural processing of the features. It is shown that the recursive landmark search approximates very well any smooth 2-D shape contour, that the shape features used are independent of perspective transformation, and that, when combined with a fuzzy ART classifier, unknown features can be efficiently learned on-line to identify multiple distinct objects. An illustrative example is used to demonstrate effectiveness of the proposed algorithm.
Publication Date
10-1-2003
Publication Title
Neural Processing Letters
Volume
18
Issue
2
Number of Pages
81-95
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1023/A:1026261202044
Copyright Status
Unknown
Socpus ID
0345328121 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0345328121
STARS Citation
Saengdeejing, Apiwat; Qu, Zhihua; and Chaeroenlap, Nopphamas, "2-D Shape Recognition Using Recursive Landmark Determination And Fuzzy Art Network Learning" (2003). Scopus Export 2000s. 1566.
https://stars.library.ucf.edu/scopus2000/1566