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Trace inference, curvature consistency, and curve detection (1989)
| Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
| Citations: | 240 - 15 self |
BibTeX
@ARTICLE{Parent89traceinference,,
author = {Pierre Parent and Steven W. Zucker},
title = {Trace inference, curvature consistency, and curve detection},
journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
year = {1989},
volume = {11},
pages = {823--839}
}
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Abstract
We describe a novel approach to curve inference based on curvature information. The inference procedure is divided into two stages: a trace inference stage, to which this paper is devoted, and a curve synthesis stage, which will be treated in a separate paper. It is shown that recovery of the trace of a curve requires estimating local models for the curve at the same time, and that tangent and curvature information are sufficient. These make it possible to specify powerful constraints between estimated tangents to a curve, in terms of a neigh-borhood relationship called cocircularity and between curvature esti-mates, in terms of a curvature consistency relation. Because all curve information is quantized, special care must be taken to obtain accurate estimates of trace points, tangents and curvatures. This issue is ad-dressed specifically by the introduction of a smoothness constraint and a maximum curvature constraint. The procedure is applied to two types of images, artificial images designed to evaluate curvature and noise sensitivity, and natural images.
Keyphrases
trace inference curvature consistency curvature information powerful constraint trace inference stage maximum curvature constraint curve synthesis stage novel approach local model natural image smoothness constraint neigh-borhood relationship inference procedure trace point special care accurate estimate curve information noise sensitivity separate paper artificial image curvature esti-mates curvature consistency relation
