Corner detection and curve partitioning using arc-chord distance

Authors:
Majed Marji;Reinhard Klette;Pepe Siy
Affiliations:
Daimler Chrysler Corporation, Auburn Hills, MI;CITR, The University of Auckland, Auckland, New Zealand;Daimler Chrysler Corporation, Auburn Hills, MI
Venue:
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Year:
2004

Citing 8
Cited 0

Perceptual Organization and Curve Partitioning

IEEE Transactions on Pattern Analysis and Machine Intelligence
A method of curve partitioning using arc-chord distance

Pattern Recognition Letters
On the Detection of Dominant Points on Digital Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
A piecewise linear approximation method preserving visual feature points of original figures

CVGIP: Graphical Models and Image Processing
Contour shape description based on an arch height function

Pattern Recognition
Detecting the dominant points by the curvature-based polygonal approximation

CVGIP: Graphical Models and Image Processing
Chord-to-point distance accumulation and planar curvature: a new approach to discrete curvature

Pattern Recognition Letters
Locating Perceptually Salient Points on Planar Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are several algorithms for curve partitioning using the arc-chord distance formulation, where a chord whose associated arc spans k pixels is moved along the curve and the distance from each border pixel to the chord is computed. The scale of the corners detected by these algorithms depends on the choice of integer k. Without a priori knowledge about the curve, it is difficult to choose a k that yields good results. This paper presents a modified method of this type that can tolerate the effects of an improper choice of k to an acceptable degree.