Feature extraction of volume data based on multi-scale representation
Pages 175 - 180
Abstract
In this paper, we present a novel algorithm that can detect and extract salient points as features in 3-D volume datasets. This algorithm extracts not only the locations of feature points, but also finds out the scales at which the features are most significant. It applies the scale-space theory by adaptively processing the input volume in a few discrete scales. The features points, as well as their scales, detected can be used for subsequent processing, such as content-based volume data authentication and content-based volume rendering.We have implemented the algorithm and tested its effectiveness on several volume datasets. Some optimization has also been done on time-consuming intermediate steps to speed up the feature detection process.
References
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LINDEBERG, T. 1990. Scale-space for discrete signals. IEEE Trans. Pattern Analysis and machine Intelligence 12, 3, 234-254.
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Index Terms
- Feature extraction of volume data based on multi-scale representation
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February 2003
307 pages
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Published: 11 February 2003
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GRAPHITE03
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GRAPHITE03: International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia
February 11 - 14, 2003
Melbourne, Australia
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Overall Acceptance Rate 124 of 241 submissions, 51%
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