Titlebook: Variational Methods in Image Segmentation; with seven image pro Jean Michel Morel,Sergio Solimini Book 1995 Birkh?user Boston 1995 Mathemat

Objective

書目名稱Variational Methods in Image Segmentation影響因子(影響力)




書目名稱Variational Methods in Image Segmentation影響因子(影響力)學科排名




書目名稱Variational Methods in Image Segmentation網(wǎng)絡公開度




書目名稱Variational Methods in Image Segmentation網(wǎng)絡公開度學科排名




書目名稱Variational Methods in Image Segmentation被引頻次




書目名稱Variational Methods in Image Segmentation被引頻次學科排名




書目名稱Variational Methods in Image Segmentation年度引用




書目名稱Variational Methods in Image Segmentation年度引用學科排名




書目名稱Variational Methods in Image Segmentation讀者反饋




書目名稱Variational Methods in Image Segmentation讀者反饋學科排名

合群

少量

1421-1750 tion of digital images as well as the understanding of visual perception. A common formalism for these theories and algorithms is obtained in a variational form. Thank to this formalization, mathematical questions about the soundness of algorithms can be raised and answered. Perception theory has to

太空

Region and Edge Growinge three above-mentioned kinds of growing methods and we shall, as in the last section, associate with them a variational formulation. This formulation is obtained by identifying for each method an energy for which the growing process appears as a steepest descent algorithm.

coagulate

Semicontinuity Properties of the Hausdorff Measure Hausdorff measure is lower semicontinuous, that is, . We show that this last property is true when the sets A. are “uniformly concentrated” (a property which we shall show to be true in Chapter 15 of this book for minimizing sequences of segmentations.

ALB

詞匯

Small Oscillation Coverings and the Excision Methodsuccessfully and decreases the Mumford-Shah energy..Several applications of this general excision principle will be done in the next two chapters. The “concrete properties” of the minimizers which will be proved therein will in fact be straightforward applications of the excision method.

Pulmonary-Veins

Linear and Nonlinear Multiscale Filtering is that “edge detection” is a kind of differentiation; now, the image must be filtered (and smoothed) before differentiation. Thus “edge detection” becomes multiscale, like the filtering, and any edge detection theory is directly deduced from a multiscale filtering theory. We show how variational f

Banquet

Region and Edge Growingth small regions and then grows them according to some homogeneity criterion. The general idea of edge growing methods is to create an initial set of (“fine scale”) edges (e.g., by one of the above-mentioned theories) and to connect the edges iteratively, according to some criterion based on proximi

overshadow

Variational Theories of Segmentationin Computer Vision. We shall now analyse several classical segmentation methods which have been directly defined by a variational formulation. This will give a final confirmation of the well-foundedness of the energy functionals which are discussed in this book.