Abstract
This paper presents a novel hardware architecture based on generalized Hebbian algorithm (GHA) for texture classification. In the architecture, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. Both the weight vector updating process and principle component computation process can also operate concurrently to further enhance the throughput. The proposed architecture has been embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.
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Lin, SJ., Hung, YT., Hwang, WJ. (2010). Efficient GHA-Based Hardware Architecture for Texture Classification. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_22
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DOI: https://doi.org/10.1007/978-3-642-16732-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16731-7
Online ISBN: 978-3-642-16732-4
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