Abstract
In this paper, an image edge detection technique based on the ant colony system (ACS) is implemented. ACS is one of the many ant algorithms of Ant Colony Optimization (ACO). The number of artificial ants, the total step number for each ant and the size of ant memory used in ACS is determined by applying genetic algorithm. Several reproductions of input image are obtained by nonlinear contrast enhancement applied to the input image. More than one image is passed through ACS and the outputs are integrated onto each other to generate one output image. A global threshold is applied to this very last image in order to obtain binary edge image.
Keywords
Ant colony optimization algorithms
Ant colony
Computer science
Genetic algorithm
Image (mathematics)
Artificial intelligence
Edge detection
Binary image
Enhanced Data Rates for GSM Evolution
Binary number
Image segmentation
Algorithm
Image processing
Pattern recognition (psychology)
Mathematics
Machine learning
Subject Areas
Image Enhancement Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Metaheuristic Optimization Algorithms Research
·Artificial Intelligence ·Physical Sciences
Color Science and Applications
·Atomic and Molecular Physics, and Optics ·Physical Sciences
Citations by Year
2013: 1
Loading…
Loading…