Hybrid evolutionary ridge regression approach for high-accurate corner extraction

Authors:
Gustavo Olague;Benjamín Hernández;Enrique Dunn
Affiliations:
EvoVisión Project, Computer Science Department, CICESE, Research Center, Applied Physics Division, Ensenada, B.C., México;EvoVisión Project, Computer Science Department, CICESE, Research Center, Applied Physics Division, Ensenada, B.C., México;EvoVisión Project, Computer Science Department, CICESE, Research Center, Applied Physics Division, Ensenada, B.C., México
Venue:
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Year:
2003

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Abstract

Corner measurement is of main concern within the following tasks: camera calibration, image matching, object tracking, recognition and reconstruction. This paper presents a hybrid evolutionary ridge regression approach for the problem of corner modeling. We search model parameters characterizing L-corner models by means of fitting the model to the image data. As the model fitting relies on an initial parameter estimation, we use a global approach to find the global minimum. Experimental results applied to an L-corner using several levels of noise show the advantages and disadvantages of our evolutionary algorithm compared to down-hill simplex and simulated annealing.