Watershed Segmentation Algorithm Based on Distance Transform

This example introduces a watershed segmentation algorithm based on distance transform, designed to effectively separate overlapping objects. The algorithm leverages the principle of distance transform by calculating the distance between object pixels and background pixels to determine segmentation boundaries. In implementation, the distance transform typically generates a topographic map where pixel values represent distances to the nearest background, with peak values indicating object centers. These peaks serve as markers for the marker-controlled watershed transformation, preventing over-segmentation. The algorithm processes images through key steps: binary image preprocessing, distance transform computation using functions like cv2.distanceTransform(), local maxima identification for marker generation, and watershed transformation execution. This approach enables more accurate segmentation of complex scenes and provides precise results for computer vision applications. The distance-transform-based watershed algorithm finds extensive applications in target detection, image analysis, and medical image processing, particularly useful for separating connected components in biomedical images or industrial inspection scenarios.