.png){kind=icon}
.png){kind=icon}
Return
Lie generators for computing steerable functions
.png){kind=icon}
OA
AI
DOI：10.1016/S0167-8655(97)00156-6.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a computational, group-theoretic approach to steerable functions. The approach is group-theoretic in that the treatment involves continuous transformation groups for which elementary Lie group theory may be applied. The approach is computational in that the theory is constructive and leads directly to a procedural implementation. For functions that are steerable with n basis functions under a k-parameter group, the procedure is efficient in that at most nk + 1 iterations of the procedure are needed to compute all the basis functions. Furthermore, the procedure is guaranteed to return the minimum number of basis functions. If the function is not steerable, a numerical implementation of the procedure could be used to compute basis functions that approximately steer the function over a range of transformation parameters. Examples of both applications are described. (C) 1998 Elsevier Science B.V.
Keywords:
steerable filters
filter design
low-level vision
feature detection
Lie groups
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W
Organization
No organization information available