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Feature detection with automatic scale selection (1998)
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| Venue: | International Journal of Computer Vision |
| Citations: | 723 - 34 self |
BibTeX
@ARTICLE{Lindeberg98featuredetection,
author = {Tony Lindeberg},
title = {Feature detection with automatic scale selection},
journal = {International Journal of Computer Vision},
year = {1998},
volume = {30},
pages = {79--116}
}
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Abstract
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is proposed for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which
Keyphrases
automatic scale selection feature detection seminal work image data local appropriate scale utmost importance likely candidate normalized derivative different way major mechanism automatic method systematic methodology traditional scale-space theory building so-called scale-space representation general principle local extremum different combination different scale unknown measurement data scale level interesting structure important implication image structure
