The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 123–126, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 123–126, 2013

  24 Sep 2013

24 Sep 2013


M. Davoodianidaliki1, A. Abedini2, and M. Shankayi1 M. Davoodianidaliki et al.
  • 1Dept. of Geomatics Engineering, Faculty of engineering, University of Tehran N. Kargar St. Jalale al e Ahmad St., Tehran, 14395515, Iran
  • 2Faculty of Aerospace Engineering and Geodesy, Institute of Geodesy, University of Stuttgart, Stuttgart 70174, Germany

Keywords: Edge Detection, Image Processing, Ant Colony Optimization, Artificial Intelligence

Abstract. Edges contain important information in image and edge detection can be considered a low level process in image processing. Among different methods developed for this purpose traditional methods are simple and rather efficient. In Swarm Intelligent methods developed in last decade, ACO is more capable in this process. This paper uses traditional edge detection operators such as Sobel and Canny as input to ACO and turns overall process adaptive to application. Magnitude matrix or edge image can be used for initial pheromone and ant distribution. Image size reduction is proposed as an efficient smoothing method. A few parameters such as area and diameter of travelled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.