Research of an Improved Snail Image Recognition Method Based on Grayscale Template Matching

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Abstract:

As the lack in the accuracy and speed of the template matching algorithm for the snail image in the complex environment, the snail source image and the template image have the appropriate scaling in order to improve their sizes in the traditional algorithm. The new algorithm avoids the very big and accurate characteristics about the snail images through shrinking the source images down. The grayscale template matching method is put forward based on the traditional template selection set to prevent that the error caused by human factors on the selected template, the redundancy between the templates is removed in a large extent, further the accuracy of the matching is improved, and the matching time is reduced greatly in the case of matching accuracy guarantee.

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700-704

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October 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Ma Wei, Wei-gen Liao, Shang-fuKuang, Si-bin Xiao, Pei-on Li: Resources and environment in the Yangtze Basin, Vol 3(2009), p.: 264-269, in Chinese.

Google Scholar

[2] Li Zhao-Jun· Study between the vegetation in Poyang Lake Area and the SnailDistribution Relationship· Jiangxi: Nanchang University, (2007), pp.1-10, in Chinese.

Google Scholar

[3] Xiao-ChuangSong. Research of image matching algorithm based on gray-scale and geometry. HeBei: HeBei University of technology, (2008 ), p, 8-12, in Chinese.

Google Scholar

[4] D I Barnea and H F Silverman. A Class of Algorithm for Fast Digital Registration. IEEE Transaction on Computers, C-21(1972), pp.179-186.

DOI: 10.1109/tc.1972.5008923

Google Scholar

[5] A Rosenfeld and AC Kak. Digital Picture Processing. Digital Picture Processing 2nd,Academic Press, Orlanrdo, Fl, USA, (1982).

DOI: 10.1016/b978-0-12-597302-1.50009-8

Google Scholar

[6] J. P. Lewis*. Fast Normalized Cross-Correlation. Industrial Light & Magic Vision Interface, (1995).

Google Scholar

[7] Yan-songWang, Qiu-qiRuan . Application of Image orient- ation and Matching Algorithm Based on Correlative Matching Method, Northern Jiao Tong University, Vo1. 26(2002), pp.20-24, in Chinese.

Google Scholar

[8] Guo-binChen, Guang-quanZhang. Application of Feature Weighted Template Matching Method in Note Character RecognitionAnalysis . Microelectronicsand Computer, vol 3(2013), pp.115-117, in Chinese.

Google Scholar

[9] Jun-linFei, Wang-xinYu, Zhi-zhongWang . An Improved Eye Feature Location Algorithm Based on Template Matching. Computer Engineering and Applications2007, 43(32): 207-213, in Chinese.

Google Scholar

[10] Li-Qiang, and Zhang Bo . A Fast Matching Algorithm Based on Image Gray Value. Computer Engineering, vol 17 (2)(2006), pp.216-221, in Chinese.

Google Scholar

[11] Jin-fengLiu . Study on fast algorithm of image template matching. Hunan: Central South University, 2007, pp.1-55, in Chinese.

Google Scholar