An ant colony optimization algorithm for image edge detection

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

An edge is a collection of linked pixels lying between boundaries of two regions. It is a local concept but the boundary of an edge is a universal concept. An ideal edge is a group of pixels located at an orthogonal step transition in gray level. Blurry edges are also acquired by the factors like problems or imperfections happened at the time of optics, sampling and image acquisition systems etc. So, edges can be closely seen as having a profile as that of ramp-like profile. Ant colony optimization is an algorithm which is inspired by the natural foraging behavior and activities of ants. ACO is mainly introduced here to tackle the image edge detection problem. The proposed approach generates a matrix, called as pheromone matrix that represents the edge information which is stored at each pixel according to the movement of ants. The movements of these ants can be determined by local changes in the intensity value of pixel.

Wseas Transactions on Signal Processing, 2010

Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging behavior of ants to find approximate solutions to difficult optimization problems. It can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. In this paper, an edge detection technique that is based on ACO is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image's intensity values. The proposed ACO-based edge detection method takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system. Experimental results show the success of the technique in extracting edges from a digital image.

Ant Colony optimization (ACO) is the technique which is used for solving computational problems and finding the best paths through graphs. ACO is based on the behavior ofants seeking paths from their colony to their food. Ants move randomly and after getting their food return back to their colony while laying down pheromone trails. Other ants find such a path and follow trail for returning. Pheromones are used for ant's communication. This technique is used for optimization in many applications like edge detection, network packet routing, structure health monitoring, vehicular routing, image segmentation traveling salesman problem, quadratic assignment problem, sequential ordering, scheduling, graph coloring, management of communications networks, image compression etc. In this paper we are using a method using ACO to find edge detection. It gives a pheromone matrix and memory stored positions that are followed by leading ant. The memory based positions are stored on the basis of intensity values with reference with a threshold value. The results are shown which successfully detect the edges of the image.

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 traveled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.

Pattern Recognition Letters, 2008

Edge detection is a technique for marking sharp intensity changes, and is important in further analyzing image content. However, traditional edge detection approaches always result in broken pieces, possibly the loss of some important edges. This study presents an ant colony optimization based mechanism to compensate broken edges. The proposed procedure adopts four moving policies to reduce the computation load. Remainders of pheromone as compensable edges are then acquired after finite iterations. Experimental results indicate that the proposed edge detection improvement approach is efficient on compensating broken edges and more efficient than the traditional ACO approach in computation reduction.

IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 2010

Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant colonies in nature. Like their biological counterparts, a colony of artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and discovery of a new one. In this paper, one of the basic ACO algorithms, the Ant System algorithm, was applied for edge detection where the edge pixels represent food for the ants. A set of grayscale images obtained by a nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images were used to create an environmental setup in which the ants move, the colony's adaptive behavior could be demonstrated on any type of digital habitat.

2012

Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species, which deposit pheromone on the ground to guide their foraging. In this paper, the algorithm proposed by Jing Tian for edge detection using ant colony optimization is proposed. The experimental results obtained through the implementation are matched with the author's results to verify the claim to be true.

International Journal of Computer Applications, 2013

Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Experimental results are provided to support the superior performance of the proposed approach.

International Journal of Computer Applications, 2013

The detection of the edge is one of the important part in the field of Image Processing. In this paper we proposed an improved ACO algorithm for digital images edge classification. The classification is basically done as per the natural phenomenon of the movement of ants for searching paths. We have proposed a new modified ACO algorithm for better visual effects and compared the experimental results with previous standard one.

The social insect metaphor for solving diverse problems has become an emerging issue in the current years emphasizing on stochastic construction process building the solution probabilistically. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants deposits a volatile chemical call pheromone on the ground surface for the purpose of foraging and collective interaction via indirect communication. Edge detection mainly is the set of mathematically methods aiming to identify points in an image at which the image brightness changes sharply or formally generating some form of discontinuities. This paper explores the swarm computing technique called Ant Colony Optimization (ACO) for edge detection of imagery.