Sub-image Matching
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In the given context, we need to ensure proper matching between main images and sub-images while identifying their precise locations within the document. This step is critical as it enables accurate image localization and facilitates the extraction of required information. Through this matching and localization process, we can achieve better comprehension of the document's content while obtaining more detailed information. Implementation typically involves image processing algorithms such as template matching using cross-correlation methods, where the sub-image serves as a template that is slid over the main image to find the position with the highest similarity score. Key functions like OpenCV's matchTemplate() can be employed with methods such as TM_CCOEFF_NORMED for normalized cross-correlation calculations, ensuring robust matching even under varying lighting conditions.
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