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Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features

Michael Felsberg, Per-Erik Forssén, Hanno Scharr
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume 28, Number 2, Pages 209-222
February 2006


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: non-linear 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 non-linear spaces, such as orientation space.

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Bibtex entry

	Author = {Michael Felsberg and Per-Erik Forss{\'e}n and Hanno Scharr},
	Journal = {{IEEE} Transactions on Pattern Analysis and Machine
	Title = {Channel Smoothing: Efficient Robust Smoothing of Low-Level
                 Signal Features},
	Year = {2006},
	Number = {2},
	Pages = {209--222},
	Volume = {28},
        issn = {0162-8828},
        Month = {February}

Per-Erik Forssén

Per-Erik Forssén


Computer Vision Laboratory
Department of Electrical Engineering
Building B
Room 2D:521
SE-581 83 Linköping, Sweden
+46(0)13 285654

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