Templatematching measure the difference between a template and a region of your image. There are different way to calculate . SQDIFF Calculates Square difference Templatematching is a technique for finding areas of an image that match (are similar) to a templateimage (patch). Source image (I): The image in which we expect to find a match to the templateimageTemplateimage (T): The patch image which will be compared to the templateimage our goal is to PIV(Particle Image Velocimetry), Traction force microscopy, Templatematching (OpenCV), Export movie files using ffmpeg, Align slices in stack and autofocus plugins for imageJ Goals . In this chapter, you will learn. To find objects in an image using TemplateMatching; You will see these functions : cv.matchTemplate(), cv.minMaxLoc() Theory . TemplateMatching is a method for searching and finding the location of a templateimage in a larger image. Templatematching is a clear application for some simple supervised learning: train some classifier on the waveforms throw the predictions into a new feature, done. In this blog post you'll learn the simple trick to make templatematching using cv2.matchTemplate more robust by examining multiple scales of an image. PIV(Particle Image Velocimetry), Traction force microscopy, Templatematching (OpenCV), Export movie files using ffmpeg, Align slices in stack and autofocus plugins for imageJ The TemplateMatching block finds the best match of a template within an input image. Templatematching compares a smaller image (the template) strengths and weaknesses of templatematching. For each template the number of matches Manual; JavaDoc; Templatematching is a 'brute-force' algorithm for object recognition. Its working is simple: create a small template (sub-image) of object to be found,say a football.