Edge detection by combined canny filter with scale multiplication &amp; ant colony optimization

Manish, T I; Murugan, D.; Kumar, T. Ganesh

doi:10.1145/2393216.2393299

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…In general, the classic edge detection techniques impose a high computational cost due to the operations performed on each pixel. Recently, methods based on metaheuristics have been proposed such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization and Artificial Bee Colony ( [2], [4], [10], [11], [13], [17], [18]). Low computational effort, competitive results and task paralellization are relevant features found in these proposals.…”

Section: B Edge Detection Methodsmentioning

confidence: 99%

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New ACO algorithm for image edge detection

Martínez¹,

Buemi²

2014

6th Chilean Conference on Pattern Recognition (CCPR)

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Ant Colony Optimization (ACO) is a metaheuristic based on real behavior of ants to solve optimization problems. Edge detection plays an important role in image processing. It consists in detecting edges or contours in images which allows to extract information from them. An ACO algorithm to edge detection is proposed. Using heuristic and knowledge information and applying an improvement operator, binary images including edges detected are obtained. The algorithm was tested with several images (real and synthetic, with and without presence of noise). The results reached were competitive in terms of quality of images and required computational time.

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Section: B Edge Detection Methodsmentioning

confidence: 99%

“…This is possible using heuristic and knowledge information and an operator that improves the solution reached by the colony. Compared to similar proposals ( [2], [4], [6], [10], [13], [17]), our algorithm achieves good quality images in a short time.…”

Section: Introductionmentioning

confidence: 97%

New ACO algorithm for image edge detection

Martínez¹,

Buemi²

2014

6th Chilean Conference on Pattern Recognition (CCPR)

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“…2. The gradient amplitude and phase angle of each point are calculated according to formula (1) and formula (2). Gx and Gy are used to judge the extension direction of the gradient at the current point and the gradient phase angle between [0, 2π] is provided;…”

Section: Edge Detection Algorithm Based On Wavelet Modulus Maximamentioning

confidence: 99%

“…As the most basic feature of an image, edge is expressed as a set of pixels with step change or roof change in the gray level of the surrounding pixels in the image, indicating the end of one region and the beginning of the position of another region [1][2][3]. Gray discontinuity may be caused by physical or geometric factors, such as discontinuity of depth, surface orientation, and discrepancy of color and texture [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Edge Detection of Crack in Extra-High Arch Dam Based on Orthogonal Wavelet Construction

Zhang¹,

Zhu²,

Song³

et al. 2022

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This paper conducts a research on the wavelet construction and application of image edge detection. Taking the image edge detection algorithm based on wavelet modulus maxima as the research subject, this paper discusses the problem of dislocation phenomenon, threshold selection, multi-scale edge fusion and evaluation criterion in the algorithm, and proposes an improved self-adaptive hierarchical threshold algorithm based on information amount and vanishing moment. From the angle of wavelet symmetry, filter composition and vanishing moment, the influence of wavelet property on image edge detection is studied, and the construction requirements of wavelet filter banks for image edge detection are proposed. The constant-length compactly supported biorthogonal wavelet parameterized construction method is used to construct the biorthogonal wavelet featuring odd symmetry for high-pass decomposition filter and unidirectionality for filter sequence, and the improved algorithm of wavelet construction and threshold is applied to the simulation of image edge detection of concrete cracks in ultra-high arch dam.

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“…The quality of the fusion images was improved for better differentiability in terms of contrast and features of the targets (11). Canny fi lter with scale multiplication and colony optimization techniques (12,13). The Gaussian noise can be reduced using a spatial fi lter and frequency fi lter (14).…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical system of image enhancement for digital microscopic pneumonia bacteria images

Kumar¹,

Asha²,

Manish³

et al. 2018

BLL

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In this paper the image processing method is used to enhance the pneumonia bacteria images. This paper recognized the bacteria images based on two domains. The enhancement techniques used for bacteria image enhancement were median filter, wiener filter, single scale retinex and multiscale retinex. Image enhancement has a very important role in digital image processing. The median and wiener filters were used for grayscale image enhancement. Then single scale retinex and multiscale retinex were used for color image enhancement. Based on performance metrics identified median filter is suitable for bacteria images in grayscale image enhancement and multiscale retinex is suitable for bacteria color image enhancement (Tab. 2, Fig. 8, Ref. 21).

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Edge detection by combined canny filter with scale multiplication & ant colony optimization

Cited by 7 publications

References 4 publications

New ACO algorithm for image edge detection

New ACO algorithm for image edge detection

Multi-Scale Edge Detection of Crack in Extra-High Arch Dam Based on Orthogonal Wavelet Construction

Empirical system of image enhancement for digital microscopic pneumonia bacteria images

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