Adaptive smoothing: a general tool for early vision
Saint-Marc, P.
Chen, J.-S.
Medioni, G.
Inst. for Robotics & Intelligent Syst., Univ. of Southern California, Los Angeles, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1991
Volume: 13,
Issue: 6
On page(s): 514-529
ISSN: 0162-8828
References Cited: 39
CODEN: ITPIDJ
INSPEC Accession Number: 3991725
Digital Object Identifier: 10.1109/34.87339
Current Version Published: 2002-08-06
Abstract
A method to smooth a signal while preserving discontinuities is
presented. This is achieved by repeatedly convolving the signal with a
very small averaging mask weighted by a measure of the signal continuity
at each point. Edge detection can be performed after a few iterations,
and features extracted from the smoothed signal are correctly localized
(hence, no tracking is needed). This last property allows the derivation
of a scale-space representation of a signal using the adaptive smoothing
parameter k as the scale dimension. The relation of this
process to anisotropic diffusion is shown. A scheme to preserve
higher-order discontinuities and results on range images is proposed.
Different implementations of adaptive smoothing are presented, first on
a serial machine, for which a multigrid algorithm is proposed to speed
up the smoothing effect, then on a single instruction multiple data
(SIMD) parallel machine such as the Connection Machine. Various
applications of adaptive smoothing such as edge detection, range image
feature extraction, corner detection, and stereo matching are discussed
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