Implementing Watershed Algorithm Through Three Distinct Approaches
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This article explores three distinct implementations of the watershed algorithm to achieve more precise image segmentation. These methodologies include: the standard watershed segmentation approach, a two-phase watershed method incorporating gradient information, and a three-phase watershed algorithm that combines gradient data with masking techniques. By implementing these algorithms, we can effectively separate different regions within images, thereby enhancing segmentation accuracy. From a coding perspective, the basic watershed implementation typically uses the watershed() function with binary markers, while the gradient-based approach incorporates Sobel or Canny edge detection to create improved gradient maps before watershed processing. The three-phase method involves additional masking operations to constrain flooding regions, often implemented through morphological operations and region masking functions to prevent over-segmentation.
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