New hypothesis distinctiveness measure for better ellipse extraction

Authors:
Cuilan Wang;Timothy S. Newman;Chunguang Cao
Affiliations:
Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL;Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL;Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 12
Cited 1

A survey of the Hough transform

Computer Vision, Graphics, and Image Processing
Partitioned Hough transform for ellipsoid detection

Pattern Recognition
A new curve detection method: randomized Hough transform (RHT)

Pattern Recognition Letters
A probabilistic Hough transform

Pattern Recognition
Randomized Hough transform: improved ellipse detection with comparison

Pattern Recognition Letters
A real-time Hough-based method for segment detection in complex multisensor

Real-Time Imaging
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
Error propagation for the Hough transform

Pattern Recognition Letters
Compound Extraction and Fitting Method for Detecting Cardiac Ventricle in SPECT Data

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
An effective voting method for circle detection

Pattern Recognition Letters
The Distinctiveness of a Curve in a Parameterized Neighborhood: Extraction and Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Ellipses by a Modified Hough Transformation

IEEE Transactions on Computers

Edge curvature and convexity based ellipse detection method

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new method for determination of the best hypothesis in Hough transform (HT)-based methods to detect ellipses is described. The method uses a new distinctiveness measurement to rank the hypotheses and determine a suitable candidate. The method is aimed at improving HT robustness to image noise and quantization effects. Experiments on images that demonstrate the method's applicability are also presented.