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| Journal Article | PreJuSER-47026 |
; ;
2006
IEEE
New York, NY
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Please use a persistent id in citations: doi:10.1109/TPAMI.2006.29
Abstract: In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: It has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.
Keyword(s): Algorithms (MeSH) ; Artificial Intelligence (MeSH) ; Data Compression: methods (MeSH) ; Image Enhancement: methods (MeSH) ; Image Interpretation, Computer-Assisted: methods (MeSH) ; Numerical Analysis, Computer-Assisted (MeSH) ; Pattern Recognition, Automated: methods (MeSH) ; Signal Processing, Computer-Assisted (MeSH) ; J ; robust smoothing (auto) ; channel representation (auto) ; diffusion filtering (auto) ; bilateral filtering (auto) ; mean-shift (auto) ; B-spline (auto) ; orientation smoothing (auto)
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