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- W2070700593 abstract "There are two basic principles associated with multiscale geometry: 1) geometry involves analysis that is invariant to certain spatial transformations, including translation, rotation, and zoom, and 2) the dimension of scale is as critical as the dimensions giving spatial position, corresponding intuitively to level of detail in image space. Considered together, image space and scale is called scale space. Three families of methods based on these principles are achieving impressive results in image analysis —particularlyin their insensitivity to irrelevant detail (including image noise) and intensity blurring, and in their ability to produce stable object descriptions and pixel classifications into objects. The three families are multiscale medial axis or core-based analysis (CBA), variable conductance difftision (VCD), and multiscale geometric statistical pattern recognition (MGSPR). This pair of tutorials (morning and afternoon) covered the basic mathematics of multiscale geometry as well as all three of these families of methods. It included algorithms for computation and also illustrative results of the applications of the methods to both 2D and 3D medical images of various modalities. The morning tutorial covered the mathematics of diffusion and scale space and the definition, effect on scale space geometry, and application of cores. The afternoon tutorial covered the mathematics, algorithms, and applications of variable conductance diffusion, including approaches involving MGSPR, and it covered algorithms for segmenting objects both via VCD and via CBA." @default.
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- W2070700593 date "1994-09-09" @default.
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- W2070700593 title "<title>Multiscale geometric image analysis: diffusion and cores and variable conductance diffusion and object calculation</title>" @default.
- W2070700593 doi "https://doi.org/10.1117/12.185237" @default.
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