Implementation and Applications of Tri-State Self-Organizing Maps on FPGA

被引:23
作者
Appiah, Kofi [1 ]
Hunter, Andrew [1 ]
Dickinson, Patrick [1 ,3 ]
Meng, Hongying [2 ]
机构
[1] Lincoln Univ, Lincoln Sch Comp Sci, Lincoln LN6 7TS, England
[2] Brunel Univ, Sch Engn & Design, Uxbridge UB8 3PH, Middx, England
[3] Lincoln Univ, Games Res Grp, Lincoln LN6 7TS, England
关键词
Binary self-organizing map (SOM); character recognition; field programmable gate array (FPGA); object recognition;
D O I
10.1109/TCSVT.2012.2197077
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper introduces a tri-state logic self-organizing map (bSOM) designed and implemented on a field programmable gate array (FPGA) chip. The bSOM takes binary inputs and maintains tri-state weights. A novel training rule is presented. The bSOM is well suited to FPGA implementation, trains quicker than the original self-organizing map (SOM), and can be used in clustering and classification problems with binary input data. Two practical applications, character recognition and appearance-based object identification, are used to illustrate the performance of the implementation. The appearance-based object identification forms part of an end-to-end surveillance system implemented wholly on FPGA. In both applications, binary signatures extracted from the objects are processed by the bSOM. The system performance is compared with a traditional SOM with real-valued weights and a strictly binary weighted SOM.
引用
收藏
页码:1150 / 1160
页数:11
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