Directional approach and modified self-adaptive ant colony optimization for edge detection

This study performed the detection of edge using directional approach and modified self-adaptive ant colony optimization. Edge detection is one of the important parts of image processing. It is essentially involved in the pre-processing stage of image analysis and computer vision. It generally detects the contour of an image and thus provides important details about an image. So, it reduces the content to process for the high-level processing tasks like object recognition and image segmentation. The most important step in the edge detection, on which the success of generation of true edge map depends, lies on the determination of parameters and initial weights. For the determination of pheromone matrix, a derivative directional approach is used. To find the suitable direction in which the ant should move and to differentiate between the erroneous pixel and a real edge pixel a directional approach is used. The proposed directional approach and modified self-adaptive ant colony optimisation for edge detection is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image´s intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach. The improved ant colony algorithm with self-adaptive adjusting the constants α and β represent the influence of pheromone information and heuristic information, respectively improves the deficiency of ant colony algorithm, such as stagnation behaviour and long search time. A comparison with other standard operators is also discussed and the proposed method produced acceptable results within reasonable amounts of time.