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AI
This research presents a modified Ant System algorithm aimed at addressing the challenges associated with broken edge linking in image processing. The proposed method incorporates a grayscale visibility matrix to enhance the evaluation of edge segments, focusing on optimizing the number of pixels and their grayscale characteristics. Experiments conducted on various test images demonstrate the effectiveness of the approach, which reduces computational load and improves edge connectivity compared to conventional methods.
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.
This paper presents a technique inspired by swarm methodologies such as ant colony algorithms for processing simple and complicated images. It is shown that the proposed technique for image processing is capable of performing feature extraction for edge detection and segmentation, even in the presence of noise. Our proposed approach, Ant-based Correlation for Edge Detection (ACED), is tested on different samples and the results are compared to typical established non-swarm-based methods. The comparative analysis highlights the advantages of the proposed method which generates less distortion when noise is added to the test images. Both qualitative and quantitative evaluations support the claim, confirming the significance of our swarm-based method for image feature extraction and segmentation.
Comput. Sci. J. Moldova, 2015
Ant Colony Optimization (ACO) is an optimization algorithm inspired by the behavior of real ant colonies to approximate the solutions of difficult optimization problems. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed approach is based on the distribution of ants on an image; ants try to find possible edges by using a state transition function. Experimental results show that the proposed method compared to standard edge detectors is less sensitive to Gaussian noise and gives finer details and thinner edges when compared to earlier ant-based approaches.
2019
Edge detection is an important topic in computer vision and image processing, and has many applications in the related areas. An edge can be defined as a group of connected pixels lying between boundaries of two regions. Edge can also be defined as in binary images as the black pixels with one nearest white neighbors. An edge can be characterized as a gathering of associated pixels locating between limits of two districts. Edge can likewise be characterized as in parallel pictures as the black pixels with one closest white neighbour. An Edge is a local concept yet the limit is a worldwide idea. Edges in image contain important information and edge detection plays an important role in image processing. Therefore, over decades lots of techniques are investigated and developed for the correct detection of edge. ACO is a method based on heuristic search and it is beneficial for discrete problems. An ACO algorithm for image edge detection has been investigated. Based on tests performed o...
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.
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.
2017
The gray level change between any two regions is described as an edge. This change is spread out among a lot of pixels. There are algorithms which can be used to detect edges. Some of them are designed in such a way that they can detect straight visible edges. Also, there were algorithms whose settings can detect partial edges. But it is very difficult to design an algorithm, which could detect partial edges and also correct edges. At the same time the algorithm should also have a setting to manage hidden edges. Multiple ant colony approach is a best method to get a good solution. This will be possible due to the cohabitation of various colonies. The activities of ant colonies that are coordinated by an interaction on the colony level is the basic idea of multiple ant colony approach.
The social insect allegory for working out problems has become a promising area in the recent years emphasizing on stochastic construction practice building the key probabilistically. The approach focuses on direct or indirect communications among uncomplicated agents. Swarm Intelligence is the collective behavior of decentralized, self-organized system whereby the joint behavior of agent interacting locally with the environment causes coherent global pattern to emerge. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants leaves a volatile substance call pheromone on the soil 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 formation of discontinuities. This paper explores the Swarm computing technique called Ant Colony Optimization (ACO) and further proposes a new technique called as Advanced Ant based Swarm Computing (AASC) for edge detection of imagery.
2009
In this paper, Ant Colony System (ACS) algorithm is applied for edge detection in grayscale images. The novelty of the proposed method is to extract a set of images from the original grayscale image using Multiscale Adaptive Gain for image contrast enhancement and then apply the ACS algorithm to detect the edges on each of the extracted images. The resulting set of images represents the pheromone trails matrices which are summed to produce the output image. The image contrast enhancement makes ACS algorithm more effective when accumulating pheromone trails on the true edge pixels. The results of the experiments are presented to confirm the effectiveness of the proposed method.
… , 2008. CEC 2008.(IEEE World Congress …, 2008
ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach 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.
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.
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.
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.
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.
2010
In this paper a new method for enhancement of digital image edge detection using ant colony optimization based on genetic algorithm has been used. In the proposed method first by the series of answers has been formed by artificial ants and then formed in a manner i.e. useful for genetic algorithm, then the answers played the role as initial population for genetic algorithm and the next population is made by genetic algorithm. Our method compared with Jing Tian method enjoys higher speed, less processing time and more answer's optimum. Also the proposed method has a better edge than other classical methods (such as sobel, etc).
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.
Edge detection is a fundamental procedure in image processing , machine vision, and computer vision. Its application area ranges from astronomy to medicine in which isolating the objects of interest in the image is of a significant importance. However, performing edge detection is a non-trivial task for which a large number of techniques have been proposed to solve it. This paper investigates the use of Ant Colony Optimization — a prominent set of optimization heuristics — to solve the edge detection problem. We propose two modified versions of the algorithm Ant Colony System (ACS) for an efficient and a noise-free edge detection.
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