In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watersheds in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation.
The most recent marker-based watershed image segmentation method is the power watershed method REF , which casts the watershed algorithm as an energy minimisation problem.
Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest
2009 IEEE 12th International Conference on Computer Vision
2009 IEEE 12th International Conference on Computer Vision
Computer Science