IEEE Trans Pattern Anal Mach Intell - Power Watershed: A Unifying Graph-Based Optimization Framework.

Tópicos

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Resumo

In this work, we extend a common framework for graph-based 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 watershed 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 the power watershed. In particular, when q=2, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasi-linear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.

Resumo Limpo

work extend common framework graphbas imag segment includ graph cut random walker shortest path optim algorithm view imag weight graph algorithm can express mean common energi function differ choic paramet q act expon differ neighbor node introduc new paramet p fix power edg weight allow us also includ optim span forest algorithm watersh framework propos new famili segment algorithm fix p produc optim span forest vari power q beyond usual watersh algorithm term power watersh particular q power watersh lead multilabel scale contrast invari uniqu global optimum obtain practic quasilinear time place watersh algorithm energi minim framework also open new possibl use unari term tradit watersh segment use watersh optim general model use applic beyond imag segment

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