Hybrid evolutionary ridge regression approach for high-accurate corner extraction

Gustavo Olague, Benjamín Hernández, Enrique Dunn

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)I/744-I/749
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - 2003
Event2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Madison, WI, United States
Duration: 18 Jun 200320 Jun 2003

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