Three Watershed Segmentation Methods for Images

This text introduces three watershed segmentation methods for images. The first method is the standard watershed segmentation approach, which typically uses marker-controlled watershed transformation to separate overlapping objects in images. Following this, we present the two-step gradient-based watershed segmentation method that applies gradient magnitude calculation as a preprocessing step to enhance edge detection before performing watershed transformation. The third method involves the three-step watershed algorithm that combines gradient operations with mask applications, where morphological operations create markers to control flooding and prevent over-segmentation. These methods represent important techniques in image segmentation that help identify distinct regions and objects within images more accurately. Implementation typically involves functions like watershed() for the segmentation core, imgradient() for gradient computation, and morphological operations like imerode() or imdilate() for marker creation.