<![CDATA[Object detection in digital images is a crucial aspect of image processing and computer vision, with applications ranging from surveillance systems and robotics to image-based search. One commonly used approach is template matching, a technique that compares a template image with sections of the target image to identify similar patterns. This study explores the implementation of the template matching method for object recognition in digital images. The process begins with image preprocessing to enhance data quality, followed by a matching procedure using normalized cross-correlation. Experimental results indicate that this method can accurately detect objects under stable lighting and scale conditions. However, its performance decreases when images undergo rotation or scale variations. Therefore, while template matching proves effective under ideal conditions, further methodological development is needed to improve its robustness against geometric transformations.s]]>
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