Pattern Recognition via PCNN and Tsallis Entropy
School of Information Science and Engineering, Southeast University, P.R. China
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Sensors 2008, 8(11), 7518-7529; https://doi.org/10.3390/s8117518
Received: 5 September 2008 / Revised: 7 November 2008 / Accepted: 17 November 2008 / Published: 25 November 2008
(This article belongs to the Special Issue Image Sensors 2009)
In this paper a novel feature extraction method for image processing via PCNN and Tsallis entropy is presented. We describe the mathematical model of the PCNN and the basic concept of Tsallis entropy in order to find a recognition method for isolated objects. Experiments show that the novel feature is translation and scale independent, while rotation independence is a bit weak at diagonal angles of 45° and 135°. Parameters of the application on face recognition are acquired by bacterial chemotaxis optimization (BCO), and the highest classification rate is 72.5%, which demonstrates its acceptable performance and potential value.
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MDPI and ACS Style
Zhang, Y.; Wu, L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors 2008, 8, 7518-7529. https://doi.org/10.3390/s8117518
AMA Style
Zhang Y, Wu L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors. 2008; 8(11):7518-7529. https://doi.org/10.3390/s8117518
Chicago/Turabian StyleZhang, YuDong; Wu, LeNan. 2008. "Pattern Recognition via PCNN and Tsallis Entropy" Sensors 8, no. 11: 7518-7529. https://doi.org/10.3390/s8117518
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