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In the early days of computer vision, pyramids were used as the main type of multi-scale representation for computing multi-scale image [[feature detection (computer vision)|features]] from real-world image data. More recent techniques include scale space representation, which has been popular among some researchers due to its theoretical foundation, the ability to decouple the subsampling stage from the multi-scale representation, the more powerful tools for theoretical analysis as well as the ability to compute a representation at ''any'' desired scale, thus avoiding the algorithmic problems of relating image representations at different resolution. Nevertheless, pyramids are still frequently used for expressing computationally efficient approximations to scale-space representation.<ref>Crowley, J, Riff O. [http://www-prima.inrialpes.fr/Prima/Homepages/jlc/papers/Crowley-ScaleSpace03.pdf Fast computation of scale normalised Gaussian receptive fields], Proc. Scale-Space'03, Isle of Skye, Scotland, Springer Lecture Notes in Computer Science, volume 2695, 2003.</ref><ref>Lindeberg, T. and Bretzner, L. [http://www.nada.kth.se/cvap/abstracts/cvap279.html Real-time scale selection in hybrid multi-scale representations], Proc. Scale-Space'03, Isle of Skye, Scotland, Springer Lecture Notes in Computer Science, volume 2695, pages 148-163, 2003.</ref><ref>Lowe, D. G., “[http://citeseer.ist.psu.edu/lowe04distinctive.html Distinctive image features from scale-invariant keypoints]”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.</ref>
==References==▼
==See also==
*[[Scale space implementation]]▼
*[[Mipmap]]
▲*[[Scale space implementation]]
▲==References==
{{reflist}}
[[Category:Image processing]]
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