This implementation uses the Open-Source Computer Vision library as a platform. In this article, we implement an algorithm that uses an object’s edge information for recognizing the object in a search image. Like edge based object recognition where the object edges are features for matching, in Generalized Hough transform, an object’s geometric features will be used for matching. Moreover, this algorithm is computationally expensive since it needs to compute the correlation between all the pixels in the template image to the search image.įeature based approach: Several methods of feature based template matching are being used in the image processing domain. Though this method is robust against linear illumination changes, the algorithm will fail when the object is partially visible or the object is mixed with other objects. Where n is the number of pixels in t(x, y) and f(x, y). The cross correlation of template t(x, y) with a sub image f(x, y) is: This is typically done at every step by subtracting the mean and dividing by the standard deviation. Gray value based approach: In gray value based matching, the Normalized Cross Correlation (NCC) algorithm is known from old days. There are mainly two approaches to solve this problem, gray value based matching (or area based matching) and feature based matching (non area based). The solution should be robust against brightness changes when an object is partially visible or mixed with other objects, and most importantly, the algorithm should be computationally efficient. Template matching is inherently a tough problem due to its speed and reliability issues. In this article, we implement an algorithm that uses an object’s edge information for recognizing the object in the search image. Template matching is an image processing problem to find the location of an object using a template image in another search image when its pose (X, Y, θ) is unknown.
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