scholarly journals Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure

2018 ◽  
Vol 6 (2) ◽  
pp. 328-336 ◽  
Author(s):  
Febri Liantoni ◽  
Rifki Indra Perwira ◽  
Daniel Silli Bataona
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Digital Image Processing ◽  
Optimization Algorithm ◽  
Bone Structure ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Image Edge Detection ◽  
Image Edge

Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobel

scholarly journals Optimization in Edge Detection using Ant Colony Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8167-8170
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Computation Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Detection Techniques ◽  
Medical Security ◽  
Satellite Applications ◽  
Image Edge

Image processing is now emerged in different fields like medical, security and surveillance, remote sensing & satellite applications and much more. Image processing includes different operations such as feature extraction, object detection and recognition, X-ray scanning etc. All such operations required edge detection to get better quality image. Edge detection is performed to distinguish different objects in an image by finding the boundaries or edges between them. Edges are used to isolate particular objects from their background as well as to recognize or classify objects. In this paper, comparison of various edge detection techniques such as Sobel, Prewitt, Roberts, Canny, LoG and Ant Colony Optimization Algorithm is given. Ant colony Optimization(ACO) use parallelism which reduces the computation time as size of an image increases.

Image Edge Detection Based on Ant Colony Optimization Algorithm

2016 ◽  
Vol 8 (1) ◽  
pp. 1-12
Author(s):  
Yin Huan
Keyword(s):  
Edge Detection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Detection Methods ◽  
Matlab Simulation ◽  
Ant Colony Optimization Algorithm ◽  
Edge Image ◽  
Noise Interference ◽  
Image Edge Detection ◽  
Image Edge

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique. The article applies the ACO to the image edge detection, get edge image edge according to different neighborhood access policy through MATLAB simulation, and use the best neighborhood strategy to get detection. Compared with the traditional edge detection methods, the algorithm can effectively suppress the noise interference, retain most of the effective information of the image.

Sign in / Sign up

Export Citation Format

Share Document