Marker-controlled watershed segmentation is a robust method for delineating objects in complex image data. This technique leverages markers to guide the watershed algorithm, reducing over-segmentation and improving accuracy. This paper presents an in-depth analysis of marker-controlled watershed segmentation, exploring its application in various domains, particularly medical imaging and remote sensing. We discuss the methodology, including preprocessing steps such as gradient computation and marker extraction. The effectiveness of this approach is demonstrated through a series of experiments on complex datasets, highlighting its advantages over traditional watershed and other segmentation techniques.

Marker-Controlled Watershed, Image Segmentation, Gradient Computation, Marker Extraction