Principles for Automatic Scale Selection
Tony Lindeberg · 1999
1Abstract: An inherent property of objects in the world is that they only exist as meaningful entities over certain ranges of scale. If one aims at describing the structure of unknown real-world signals, then a multi-scale representation of data is of crucial importance. Whereas conventional scale-space theory provides a well-founded framework for dealing with im-age structures at dierent scales, this theory does not directly address the problem of how to select appropriate scales for further analysis. This chap-ter outlines a systematic methodology of how mechanisms for automatic scale selection can be formulated in the problem domains of feature detec-tion and image matching ( ow estimation), respectively. For feature detectors expressed in terms of Gaussian derivatives, hy-potheses about interesting scale levels can be generated from scales at which normalized measures of feature strength assume local maxima with respect to scale. It is shown how the notion of -normalized derivatives arises by necessity given the requirement that the scale selection mechanism should