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Using Local Invariant in Occluded Object Recognition by Hopfield Neural Network

In our research, we proposed a novel invariant in 2-D image contour recognition based on Hopfield-Tank neural network. At first, we searched the feature points, the position of feature points where are included high curvature and corner on the contour. We used polygonal approximation to describe the image contour. There have two patterns we set, one is model pattern another is test pattern. The Hopfield-Tank network was employed to perform feature matching. In our results show that we can overcome the test pattern which consists of translation, rotation, scaling transformation and no matter single or occlusion pattern.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0711103-165401
Date11 July 2003
CreatorsTzeng, Chih-Hung
ContributorsChen-Wen Yen, Innchyn Her, Chi-Cheng Cheng
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0711103-165401
Rightswithheld, Copyright information available at source archive

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