TITLE:
Template Matching using Statistical Model and Parametric Template for Multi-Template
AUTHORS:
Chin-Sheng Chen, Jian-Jhe Huang, Chien-Liang Huang
KEYWORDS:
Multi-Template; Template Matching; Parametric Template; Normalized Cross Correlation; Principal Component Analysis; Statistical Model
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
16,
2013
ABSTRACT: 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.