Analysis on niche genetic algorithm based nonparametric curve recognition
29 Nov 2007-Vol. 6833, pp 683311
TL;DR: It is analyzed that the odd order moments of the tiles in the raw image is more sensitive to the tiles with cracks other than tiles with only noise, the algorithm complexity is sensitive to encoding approach, and the evolution converge characteristics are sensitive to sharing function and parameters in fitness function.
Abstract: Niche Genetic Algorithm (NGA) is proposed to recognize a disconnected nonparametric curve from a noisy binary
image. The fitness function used in the NGA is derived from the hypothesis: Human Visual Tradition Model (HVTM).
Sharing function based niche technique and elite-preserving strategy are utilized to preserve population variety for
converging at the global optimum. It has the advantage of using a nonparametric method to extract disconnected curves
from the noisy binary image other than the parametric method, which Hough Transform (HT) can conclude. The curve
extracted by using the nonparametric method is verified by comparing the best strings respectively along rows and
columns in the permutation-based encoding space. The curve length can be derived automatically from the image by
calculating the accumulation of the distance between the neighbor tiles in the extracted curve. In this paper, it is analyzed
that the odd order moments of the tiles in the raw image is more sensitive to the tiles with cracks other than tiles with
only noise, the algorithm complexity is sensitive to encoding approach, the evolution converge characteristics are
sensitive to sharing function and parameters in fitness function. Experimental results present that the approach was
successfully used in pavement crack detection.
References
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TL;DR: It is pointed out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further, and how the method can be used for more general curve fitting.
Abstract: Hough has proposed an interesting and computationally efficient procedure for detecting lines in pictures. This paper points out that the use of angle-radius rather than slope-intercept parameters simplifies the computation further. It also shows how the method can be used for more general curve fitting, and gives alternative interpretations that explain the source of its efficiency.
6,693 citations
Book•
01 Oct 2001
TL;DR: This book presents an introduction to Evolutionary Algorithms, a meta-language for programming with real-time implications, and some examples of how different types of algorithms can be tuned for different levels of integration.
Abstract: List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index.
2,126 citations
01 Jan 1997
TL;DR: An algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform is described, found to give improvements in accuracy, and a reduction in computation time and the number of false alarms detected.
Abstract: We describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform. The algorithm is compared to a standard implementation of the Hough Transform, and the Probabilistic Hough Transform. Tests are performed using both noise-free and noisy images, and several real-world images. The algorithm was found to give improvements in accuracy, and a reduction in computation time, memory requirements and the number of false alarms detected.
266 citations
TL;DR: In this paper, the authors describe an algorithm for the detection of ellipse shapes in images, using the Randomized Hough Transform (RHT) and compare it with three other Hough-based algorithms.
252 citations
TL;DR: A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulatedAnnealing, microcanonical annealed, and the random cost algorithm are shown to exhibit superior performance as compared with the canonical genetic algorithm.
Abstract: Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented.
204 citations