Curvature scale space with affine length parametrisation

被引：0
|
作者：
Abbasi, S [1 ]
Mokhtarian, F [1 ]
机构：
[1] Univ Surrey, Ctr Vis Speech & Signal Proc, Guildford GU2 5XH, Surrey, England
来源：
SCALE-SPACE THEORIES IN COMPUTER VISION | 1999年 / 1682卷
关键词：
curvature scale space; affine transformation; image databases; shape similarity retrieval; affine length;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The maxima of Curvature Scale Space (CSS) image have already been used to represent 2-D shapes under affine transforms. Since the CSS image employs the arc length parametrisation which is not affine invariant, we expect some deviation in the maxima of the CSS image under general affine transforms. In this paper we examine the advantage of using affine length rather than arc length to parametrise the curve prior to computing its CSS image. The parametrisation has been proven to be invariant under affine transformation and has been used in many affine invariant shape recognition methods. The CSS representation with affine length parametrisation has been used to find similar shapes from a large prototype database.
引用
收藏
页码：435 / 440
页数：6
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