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- W2502601314 abstract "This chapter deals with image segmentation using the variational and level-set methods discussed in Chapter 10. Over three decades of work in the literature (1960–1990) has been devoted to gradient-based and statistical-based approaches for detection and linking of objects’ boundaries. These approaches have a mixed record of success owing to the ill-posed nature of the problem, and they do not easily adapt to fusion of a priori information about the objects or the imaging sensors. Variational approaches provide an alternative view to purely gradient-based and statistical-based methods. These approaches extract the object boundaries through an energy minimization framework that controls the propagation of a parametric curve or surface (sometimes called a front, as indicated in Chapter 10) inside the objects of interest. Two techniques have been proposed for controlling the curve propagation: active contours (snakes), introduced by Kass, Witkin and Terzopoulos in 1987 [12.1] (see also 12.2],[12.3]), and the level-sets approach, introduced by Osher and Sethian in 1988 [12.4]. At the heart of the active contours and the level-set approaches is an energy formulation that implicitly describes the curve/surface propagation in terms of a set of partial differential equations that can be solved numerically to determine the steady state position of the front. The level-set methods (LSM) have proven to be more efficient and flexible than active contours; the image segmentation algorithms developed in this chapter will be mainly based on the LSM." @default.
- W2502601314 created "2016-08-23" @default.
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- W2502601314 date "2014-10-30" @default.
- W2502601314 modified "2023-09-27" @default.
- W2502601314 title "Segmentation: variational approach" @default.
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- W2502601314 doi "https://doi.org/10.1017/cbo9781139022675.019" @default.
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