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Although the scale-space theory presented so far provides a
well-founded framework for representing and detecting image structures
at multiple scales, it does not address the problem of how to
select locally appropriate scales for further analysis.
Whereas the problem of finding ``the best scales'' for
handling a given real-world data set may be regarded as
intractable unless further information is available,
there are many situations in which a mechanism is required for generating
hypotheses about interesting scales.
A general methodology for feature detection with automatic
scale selection has been proposed in [, ].
The approach is based on the evolution over scales
of (possibly non-linear) combinations of
normalized derivatives defined by
Figures 6-15 show a few examples of integrating this scale selection mechanism with the differential geometric feature detectors considered in section 4. Here, the extracted features have been illustrated graphically in two ways; (i) as two-dimensional spatial projections onto the image plane, and (ii) as three-dimensional entities in scale-space, with the height over the image plane representing the selected scales.
Next: Edge detection. Up: Scale-space: A framework for Previous: Corner detection and blob Tony Lindeberg
Tue Jul 1 14:57:47 MET DST 1997