Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction

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.

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Benjamín Hernández, Enrique Dunn, Gustavo Olague
• Keywords: Computer Vision, CVPR 2003, Down-hill Simplex, Evolutionary Ridge Regression, Initial Parameter Estimation, Model Fitting Relies, Model Parameters