26.10.2018
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You are here: » » Template Matching Introduction Template Matching is a high-level machine vision technique that identifies the parts on an image that match a predefined template. Advanced template matching algorithms allow to find occurrences of the template regardless of their orientation and local brightness. Template Matching techniques are flexible and relatively straightforward to use, which makes them one of the most popular methods of object localization. Their applicability is limited mostly by the available computational power, as identification of big and complex templates can be time-consuming. Concept Template Matching techniques are expected to address the following need: provided a reference image of an object (the template image) and an image to be inspected (the input image) we want to identify all input image locations at which the object from the template image is present.
Correspondence Matching. Reading: T&V. And the template before doing cross correlation. •A solution is to NORMALIZE the pixels in the windows. Template Matching is a high-level machine vision technique that identifies the parts on an. Normalized cross-correlation is an enhanced version of the classic.
Depending on the specific problem at hand, we may (or may not) want to identify the rotated or scaled occurrences. We will start with a demonstration of a naive Template Matching method, which is insufficient for real-life applications, but illustrates the core concept from which the actual Template Matching algorithms stem from. After that we will explain how this method is enhanced and extended in advanced Grayscale-based Matching and Edge-based Matching routines. Naive Template Matching Imagine that we are going to inspect an image of a plug and our goal is to find its pins. We are provided with a template image representing the reference object we are looking for and the input image to be inspected. Template image Input image We will perform the actual search in a rather straightforward way – we will position the template over the image at every possible location, and each time we will compute some numeric measure of similarity between the template and the image segment it currently overlaps with. Finally we will identify the positions that yield the best similarity measures as the probable template occurrences.
Image Correlation One of the subproblems that occur in the specification above is calculating the similarity measure of the aligned template image and the overlapped segment of the input image, which is equivalent to calculating a similarity measure of two images of equal dimensions. This is a classical task, and a numeric measure of image similarity is usually called image correlation. Image1 Image2 Cross-Correlation 194680 The fundamental method of calculating the image correlation is so called cross-correlation, which essentially is a simple sum of pairwise multiplications of corresponding pixel values of the images. Though we may notice that the correlation value indeed seems to reflect the similarity of the images being compared, cross-correlation method is far from being robust.