Organization of: ScaleSpace Theory in Computer VisionScaleSpace Theory in Computer Vision deals with the fundamental problems that are associated with the use of scalespace 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 scalespace representation. A number of fundamental results on scalespace and related multiscale representations are reviewed. The problem of how to formulate a scalespace 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 scalespace primal sketch is presented, which is a formal representation of structures at multiple scales in scalespace aimed at the making information in the scalespace 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 scalespace primal sketch can be used for guiding other lowlevel 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 threedimensional 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 scalespace smoothing, and thus increase the accuracy in the computed surface orientation estimates.
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Tony Lindeberg
