Sensors 2008, 8(11), 7518-7529; doi:10.3390/s8117518

Pattern Recognition via PCNN and Tsallis Entropy

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Received: 5 September 2008; in revised form: 7 November 2008 / Accepted: 17 November 2008 / Published: 25 November 2008
(This article belongs to the Special Issue Image Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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MDPI and ACS Style

Zhang, Y.; Wu, L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors 2008, 8, 7518-7529.

AMA Style

Zhang Y, Wu L. Pattern Recognition via PCNN and Tsallis Entropy. Sensors. 2008; 8(11):7518-7529.

Chicago/Turabian Style

Zhang, YuDong; Wu, LeNan. 2008. "Pattern Recognition via PCNN and Tsallis Entropy." Sensors 8, no. 11: 7518-7529.

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