New shape-based auroral oval segmentation driven by LLS-RHT

Authors:
Chunguang Cao;Timothy S. Newman;Glynn A. Germany
Affiliations:
Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL 35899, USA;Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL 35899, USA;Center for Space Plasma and Aeronomic Research, University of Alabama in Huntsville, Huntsville, AL 35899, USA
Venue:
Pattern Recognition
Year:
2009

Citing 11
Cited 0

Minimum error thresholding

Pattern Recognition
Detecting partially occluded ellipses using the Hough transform

Image and Vision Computing - 4th Alvey Vision Meeting
Partitioned Hough transform for ellipsoid detection

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

Pattern Recognition Letters
The dynamic generalized Hough transform: its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses

CVGIP: Image Understanding
Randomized Hough transform: improved ellipse detection with comparison

Pattern Recognition Letters
Direct Least Square Fitting of Ellipses

IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Different Thresholding Algorithms for Segmenting Auroras

ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Detection of Ellipses by a Modified Hough Transformation

IEEE Transactions on Computers
Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex

Neural Computation
Image shadow removal using pulse coupled neural network

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

A new method that exploits shape to localize the auroral oval in satellite imagery is introduced. The core of the method is driven by the linear least-squares (LLS) randomized Hough transform (RHT). The LLS-RHT is a new fast variant of the RHT suitable when not all necessary conditions of the RHT can be satisfied. The method is also compared with the three existing methods for aurora localization, namely the histogram-based k-means [C.C. Hung, G. Germany, K-means and iterative selection algorithms in image segmentation, IEEE Southeastcon 2003 (Session 1: Software Development)], adaptive thresholding [X. Li, R. Ramachandran, M. He, S. Movva, J.A. Rushing, S.J. Graves, W. Lyatsky, A. Tan, G.A. Germany, Comparing different thresholding algorithms for segmenting auroras, in: Proceedings of the International Conference on Information Technology: Coding and Computing, vol. 6, 2004, pp. 594-601], and pulse-coupled neural network-based [G.A. Germany, G.K. Parks, H. Ranganath, R. Elsen, P.G. Richards, W. Swift, J.F. Spann, M. Brittnacher, Analysis of auroral morphology: substorm precursor and onset on January 10, 1997, Geophys. Res. Lett. 25 (15) (1998) 3042-3046] methods. The methodologies and their performance on real image data are both considered in the comparison. These images include complications such as random noise, low contrast, and moderate levels of key obscuring phenomena.