Optimal edge detection using expansion matching and restoration
Raghunath Rao, K.
Ben-Arie, J.
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Dec 1994
Volume: 16,
Issue: 12
On page(s): 1169-1182
ISSN: 0162-8828
References Cited: 24
CODEN: ITPIDJ
INSPEC Accession Number: 4871841
Digital Object Identifier: 10.1109/34.387490
Current Version Published: 2002-08-06
Abstract
Discusses the application of a newly developed expansion matching
method for edge detection. Expansion matching optimizes a novel matching
criterion called the discriminative signal-to-noise ratio (DSNR) and has
been shown to robustly recognize templates under conditions of noise,
severe occlusion and superposition. The DSNR criterion is better suited
to evaluate matching in practical conditions than the traditional SNR
since it considers as “noise” even the off-center response
of the filter to the template itself. We introduce a family of optimal
DSNR edge detectors based on the expansion filter for several edge
models. For step edges, the optimal DSNR step expansion filter (SEF) is
compared with the widely used Canny edge detector (CED). Experimental
comparisons show that our edge detector yields better performance than
the CED in terms of DSNR even under very adverse noise conditions. As
for boundary detection, the SEF consistently yields higher figures of
merit than the CED on a synthetic binary image over a wide range of
noise levels. Results also show that the design parameters of size or
width of the SEF are less critical than the CED variance. This means
that a single scale of the SEF spans a larger range of input noise than
a single scale of the CED. Experiments on a noisy image reveal that the
SEF yields less noisy edge elements and preserves structural details
more accurately. On the other hand, the CED output has better
suppression of multiple responses than the corresponding SEF
output
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