Fuzzy edge detection based on pixel's gradient and standard deviation values

Barkhoda, Wafa; Tab, Fardin Akhlaqian; Shahryari, Om-Kolsoom

doi:10.1109/imcsit.2009.5352742

Cited by 32 publications

(11 citation statements)

References 15 publications

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“…The gradient vector points in the steepest direction and the magnitude at the (x,y) coordinates is defined as: (1) and the orientation denoted by f (x, y) is: (2) The major drawback of these algorithms is their high sensitivity to noise, as illustrated in the next picture. Even a low level of noise can completely hide the edge signal.…”

Section: Gradient Methodsmentioning

confidence: 99%

Comparison of pitch distance determination algorithms

Hermann

2013

2013 IEEE 9th International Conference on Computational Cybernetics (ICCC)

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Section: Gradient Methodsmentioning

confidence: 99%

Comparison of pitch distance determination algorithms

Hermann

2013

2013 IEEE 9th International Conference on Computational Cybernetics (ICCC)

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“…Fuzzy systems are based on fuzzy sets theory, where a set of rules are used to perform an inference process. Barkhoda et al [7] presented a fuzzy edge detector, using the values of gradient (GRAD) for each pixel, and the pixel standard deviation (SD) related to its 8 neighbors. The algorithm is summarized as follows: 1) Sobel's algorithm to compute the gradient (GRAD) for each image pixel;…”

Section: Fuzzy System Applied To Edge Detectionmentioning

confidence: 99%

Image Edge Extraction by Artificial Intelligence Schemes for UAV Autonomous Navigation

Silva¹,

Vijaykumar²,

Sandri³

et al. 2020

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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We present here the application of an image processing strategy for autonomous navigation in order to estimate the position of an Unmanned Aerial Vehicle (UAV) equipped with Synthetic Aperture Radar (SAR) sensors. Patches of the SAR images are compared to georeferenced satellite optical images, by first applying an image edge detection algorithm on all the images, and the UAV position is determined from the correlation matrix obtained from resulting segmented images. The performance of three edge detectors are compared: the Canny approach, an artificial neural network (radial base function), and a fuzzy system.

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“…Wafa barkhoda et al [12] introduced two different methods based on calculation of gradient and standard deviation of pixels to form two set of edges used as inputs for fuzzy system. The fuzzy system includes appropriately defined fuzzy rules and fuzzy membership functions to decide about pixel classification as edge or non-edge.…”

Section: B Fuzzy Logic Based Approachesmentioning

confidence: 99%

Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid

Kaushal¹,

Akashdeep²

2013

JETWI

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Soft Computing is a multifaceted technique comprising of Fuzzy Logic, Neural Network, Genetic algorithms and other Evolutionary computation. These paradigms have found wide variety of applications in the field of image processing. One of the most vital applications of image segmentation is edge detection where edge refers to the boundary between two consistent regions and edge detection is the process of detecting and finding abrupt discontinuities in an image. This paper summarizes hybrid and non-hybrid approaches for edge detection. The objective of this paper is to survey the core issues for soft computing based approaches for edge detection.

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Fuzzy edge detection based on pixel's gradient and standard deviation values

Cited by 32 publications

References 15 publications

Comparison of pitch distance determination algorithms

Comparison of pitch distance determination algorithms

Image Edge Extraction by Artificial Intelligence Schemes for UAV Autonomous Navigation

Reviewing Soft Computing Approaches for Edge Detection: Hybrid and Non-hybrid

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