A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner detection and curve representation using cubic B-spline
Computer Vision, Graphics, and Image Processing
Pattern Recognition
Recognizing corners by fitting parametric models
International Journal of Computer Vision
A computational approach for corner and vertex detection
International Journal of Computer Vision
Graphical Models and Image Processing
Affine Morphological Multiscale Analysis of Corners andMultiple Junctions
International Journal of Computer Vision
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Boundary-based corner detection using eigenvalues of covariance matrices
Pattern Recognition Letters
Analysis of gray level corner detection
Pattern Recognition Letters
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Evolving Task Specific Image Operator
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
A mean field annealing approach to robust corner detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parisian evolution with honeybees for three-dimensional reconstruction
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The honeybee search algorithm for three-dimensional reconstruction
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Corner feature extraction is studied in this paper as a global optimization problem. We propose a new parametric corner modeling based on a Unit Step Edge Function (USEF) that defines a straight line edge. This USEF function is a distribution function, which models the optical and physical characteristics present in digital photogrammetric systems. We search model parameters characterizing completely single gray-value structures by means of least squares fit of the model to the observed image intensities. As the identification results relies on the initial parameter values and as usual with non-linear cost functions in general we cannot guarantee to find the global minimum. Hence, we introduce an evolutionary algorithm using an affine transformation in order to estimate the model parameters. This transformation encapsulates within a single algebraic form the two main operations, mutation and crossover, of an evolutionary algorithm. Experimental results show the superiority of our L-corner model applying several levels of noise with respect to simplex and simulated annealing.