Organization of: Scale-Space Theory in Computer VisionScale-Space Theory in Computer Vision deals with the fundamental problems that are associated with the use of scale-space analysis in early processing of visual information. More specifically some of the main questions it addresses are the following:
Part I starts by considering the basic theory of scale-space representation. A number of fundamental results on scale-space and related multi-scale representations are reviewed. The problem of how to formulate a scale-space theory for discrete signals is treated, as is the problem of how to compute image features within the Gaussian derivative framework.
Then, in Part II a representation called the scale-space primal sketch is presented, which is a formal representation of structures at multiple scales in scale-space aimed at the making information in the scale-space representation explicit. The theory behind its construction is analysed, and an algorithm is presented for computing the representation.
In Part III it is demonstrated how this representation can be integrated with other visual modules. Qualitative scale and region information extracted from the scale-space primal sketch can be used for guiding other low-level processes and simplifying their tasks.
Finally, in Part IV it is shown how the suggested method for scale selectioncan be extended to other aspects of image structure, and how three-dimensional shape cues can be computed within the Gaussian derivative framework. Such information can then be used for adapting the shape of the smoothing kernel, to reduce the shape distorting effects of the scale-space smoothing, and thus increase the accuracy in the computed surface orientation estimates.
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