Template Matching Theory of Shape Based Matching Application

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2.1.1.6 Template Matching

Template matching can be classified into two phases known as classification and recognition. Classification is concerned with establishing criteria that can be used to identify or distinguish different populations of objects that appear in images. Recognition is the process by which these tools are subsequently used to find a particular feature within and image. It functions at many different levels, including such different processes as finding a pyramid object in an image or matching the object to a specific type of object Russ, 2007. Template matching is an algorithm that compares portions of images against one another. Before hand, the template of an object must be classified first before used it in recognize similar objects in source image Jurie and Dhome, 2002. Figure 2.14: Example of Harris Application 35 Figure 2.15 shows the process evaluation of the template matching by using correlation method in representing relationship between template and source images. Correlation is a measure of the degree to which two variables pixel values in template and source images agree, not necessary in actual value but in general behaviour. In correlation method, results of combination of differences between template gray level image, with average gray level in the template image, and difference between source image sections, with the average gray level of source image, ẏ are compared to the square root summation of the pixel differences between two images. Correlation value is between -1 and +1, with larger values representing a stronger relationship between the two images. Equation 2.4 shows the correlation relationship. ∑ √∑ ∑ 2.4 Figure 2.15: Template Matching Process Evaluation 36 Correlation value totally depends on template creation throughout the system. Without proper contribution on it, may result to poor recognition rate. The important of ROI extraction method that delivers the precise region helps in findings the same object from various types of images.

2.1.2 3D Recognition Object based on 2D Images