Curved object location by Hough transformations and inversions
Pattern Recognition
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Circular arc detection based on Hough transform
Pattern Recognition Letters
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Some remarks on the straight line Hough transform
Pattern Recognition Letters
A new circle/ellipse detector using genetic algorithms
Pattern Recognition Letters
Hough-transform detection of lines in 3-D space
Pattern Recognition Letters
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Randomized or probabilistic Hough transform: unified performance evaluation
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Error propagation for the Hough transform
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Corner detection based on modified Hough transform
Pattern Recognition Letters
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
Inherent Bias and Noise in the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Toward minimal restriction of genetic encoding and crossovers for the two-dimensional Euclidean TSP
IEEE Transactions on Evolutionary Computation
Journal of Visual Communication and Image Representation
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This research presents an approach utilizing niche genetic algorithms (NGA) other than Hough transform (HT) in detecting nonparametric curves or undefined shapes in a binary image. The optimum curve can be concluded from the evolutions of two populations, which are separately coded along columns and rows, or from multi-population competition. In order to extract the most probable curve as human visualization does, the fitness function based on the human visual tradition model is introduced for the fitness evaluation. The NGA-based curve feature extraction approach has many unique characteristics compared with the HT method, such as the ability to obtain the trajectory and length of nonparametric curves, high convergence speed, and implicit parallelism. For NGA-based curve extraction, this paper offers detailed analysis in the construction of fitness function, NGA, multi-population competition, population reservation, and comparison with Hough transform.