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Pattern Recognition via PCNN and Tsallis Entropy
School of Information Science and Engineering, Southeast University, P.R. China
* Author to whom correspondence should be addressed.
Received: 5 September 2008; in revised form: 7 November 2008 / Accepted: 17 November 2008 / Published: 25 November 2008
Abstract: 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.
Keywords: Pattern recognition; feature extraction; pulse coupled neural network; Tsallis entropy
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Cite This Article
MDPI and ACS Style
Zhang, Y.; Wu, L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors 2008, 8, 7518-7529.
Zhang Y, Wu L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors. 2008; 8(11):7518-7529.
Zhang, YuDong; Wu, LeNan. 2008. "Pattern Recognition via PCNN and Tsallis Entropy." Sensors 8, no. 11: 7518-7529.