Employing Thresholding and Template Rotation to Enhance the Effectiveness of Template Matching Programs

The most important first step in the majority of image analysis workflows is the localization of objects of interest. Typical picture segmentation techniques include thresholding and edge detection. Although such techniques work well for tagged objects, they are at their limit when samples have poor background contrast or when only a portion of bigger structures should be recognized. Furthermore, the creation of such pipelines necessitates extensive analysis process engineering and frequently yields case-specific solutions. This study describes how to create a two-stage OpenCV-based object counter with improved accuracy and performance by utilizing template rotation and thresholding approaches this negates certain limitation that other techniques such as edge detection has while also building on and improving the standard template matching algorithm.