Template Matching using Statistical Model and Parametric Template for Multi-Template


This paper represents a template matching using statistical model and parametric template for multi-template. This algorithm consists of two phases: training and matching phases. In the training phase, the statistical model created by principal component analysis method (PCA) can be used to synthesize multi-template. The advantage of PCA is to reduce the variances of multi-template. In the matching phase, the normalized cross correlation (NCC) is employed to find the candidates in inspection images. The relationship between image block and multi-template is built to use parametric template method. Results show that the proposed method is more efficient than the conventional template matching and parametric template. Furthermore, the proposed method is more robust than conventional template method.

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C. Chen, J. Huang and C. Huang, "Template Matching using Statistical Model and Parametric Template for Multi-Template," Journal of Signal and Information Processing, Vol. 4 No. 3B, 2013, pp. 52-57. doi: 10.4236/jsip.2013.43B009.

Conflicts of Interest

The authors declare no conflicts of interest.


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