Template Matching Algorithm Implementation For Introduction To Indonesian Traditional House
Abstract
Digital image processing is an important way in computer vision to determine the shape of digital image objects. Many image processing applications have been produced, one of which is pattern recognition. Pattern recognition can be interpreted as the process of classifying a number of objects into several categories based on similarities and similarities in characteristics. In this study, the pattern recognition of traditional houses in Indonesia will be carried out. In the pattern recognition process, several stages of image processing (image pre-processing) are needed which aims to make the pattern obtained can be recognized accurately. For the introduction of this traditional house, several stages will be carried out before it can finally be recognized by the system. First, the image will be converted into a gray image, after the gray image is obtained, it will be converted into a binary image using thresholding and then followed by normalizing the image size (resize). After that, the resized image will be recognized using the Template Matching method. The results of this study, using 20 examples of traditional house patterns categorized into 10 types of traditional houses in Indonesia, the system can recognize 16 traditional house patterns correctly with an accuracy percentage of 80%. But this system also still has imperfections. Imperfections occur due to several factors such as taking the angle of the object in the test image that does not match the angle of the object in the target image, and also the presence of additional objects that cover the shape of the traditional house that will be recognized; such as: trees, cars, etc., as well as poor test image conditions.
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