USING_AN_ANT_COLONY_OPTIMIZATION_ALGORITHM

• https://www.pirahansiah.com/farshid/portfolio/publications/Journals/USING_AN_ANT_COLONY_OPTIMIZATION_ALGORITHM/

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Ant Colony Optimization for Image Edge Detection

1. Introduction

• Thresholding: Used in various computer vision applications like OCR, image segmentation, and object tracking.
• Ant Colony Optimization (ACO): Population-based metaheuristic for optimization.
• Objective: Combining ACO, edge detection, and thresholding for Optical Character Recognition (OCR) systems.

2. State of the Art

2.1 Thresholding Methods

• Categories: Single, Multilevel, Multi-thresholding
• Single Thresholding: Converts the image into binary (black and white).
  • Pirahansiah’s Single Threshold Method: A custom single threshold method using PSNR.
• Multilevel Thresholding: Separates objects based on gray values using multiple thresholds.
• Multi-threshold: Uses multiple threshold values to identify objects in images.

2.2 Ant Colony Optimization (ACO)

• Introduction: Initially proposed by Marco Dorigo in 1992 for combinatorial optimization problems.
• Application: Used for image edge detection in this paper.
• Process:
  1. Initialize ants randomly.
  2. Move ants based on probability and pheromone updates.
  3. Update pheromone values for optimization.

3. Proposed Method

• Combining ACO and Thresholding: ACO is applied to enhance image thresholding in OCR systems.
• Comparison: The proposed method is compared with Otsu, Kittler, Illingworth, and Pirahansiah’s methods.

4. Results and Discussion

• Datasets: DIBCO 2009 benchmark, including printed and handwritten images.
• Performance: The proposed ACO-based method shows better PSNR results for thresholding compared to traditional methods.
• Comparison Results: The ACO method outperforms others in printed and handwritten datasets.

5. Conclusion

• Effective for OCR: The ACO-based thresholding method improves the edge detection and thresholding for OCR systems.
• Future Work: Optimizing ACO parameters for better performance in different types of images.