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.
Highlights
Template matching is an essential task in image processing in many applications, including remote sensing, computer vision, medical imaging, and industrial inspection
In the traditional normalized cross correlation (NCC) method, the NCC coefficient is obtained from different templates; they individually have a maximum and a minimum NCC coefficient compared to 5 templates in Case 1 and 9 templates in Case 2
The experimental results show that the proposed method is more robust than the traditional NCC method against the illumination change
Summary
Template matching is an essential task in image processing in many applications, including remote sensing, computer vision, medical imaging, and industrial inspection. The traditional NCC method is applied in the case of single template; Tanaka and Sano [2] proposed a parametric template method for template matching In this method, the parametric space is constructed from the given vertex images (multi-template) that contain rotation and scale variances, but it is a time consuming method. Lin and Chen [3] proposed parametric template vector for template matching with translation, rotation, and scale invariance using the ring-projection transform, but the rotation angle cannot be estimated. They [4] further introduced a sub-pixel template matching with rotation invariance using the parametric template and the ring-projection transform methods. Tomazevic, Pernus and Likar [7] proposed a statistical method to inspect the imprint quality that incorporates the rotation information
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