The brute-force pattern matching algorithm compares the pattern Pwith the text T for each possible shift of P relative to T, until either a match is found, or all placements of the pattern have been tried Brute-force pattern matching runs in time O(nm) while Example of worst case: T =aaa … ah P =aaah 5.3 Pattern Matching Algorithm - Step by Step 1. The raw science image template is classified by its computed median Data Number (DN), and the ITF level possessing a comparable DN value at that location is chosen for correlation purposes. 2. The brute-force pattern matching algorithm compares the pattern Pwith the text T for each possible shift of P relative to T, until either a match is found, or all placements of the pattern have been tried Brute-force pattern matching runs in time O(nm) Example of worst case: T =aaa … ah P =aaah may occur in images and DNA sequences Fastest Image Pattern Matching. The best template matching implementation on the Internet. Using C++/MFC/OpenCV to build a Normalized Cross Corelation-based image alignment algorithm. The result means the similarity of two images, and the formular is as followed: Improvements. rotation invariant; using image pyrimid as a searching strategy Image matching is an important topic in image processing. Matching technology plays an important role in and is the basis for image understanding. In order to solve the shortcomings of slow image matching and low matching accuracy, a matching method based on improved genetic algorithm is proposed. The main improvement of the algorithm is the use of self-identifying crossover operators for