Ultrasound Image Edge Detection Based on a Novel Multiplicative Gradient and Canny Operator

Zheng, Yu‐Xiang; Zhou, Yiming; Zhou, Hao; Gong, Xiaohong

doi:10.1177/0161734614554461

Cited by 26 publications

(10 citation statements)

References 15 publications

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“…Datasets can be enhanced using a group of GAN derivative models. These models also have the additional use of classifying images that show a promising result [43]. Many examples have proven their use in solving problems with a low number of samples, performing the aforementioned tasks and improving the accuracy of the trainer in many cases, such as in the detection of image forgery [44,45].…”

Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

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The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

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Section: Related Workmentioning

confidence: 99%

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

2019

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“…A large number of practices have proved that the GAN network model can be used to solve the problem of too few samples in a training set, such as Gurumurthy and other improved GAN network models employed to enhance small data sets and improve the classification accuracy of the trainer [14]. Domestic scholars have proposed several GAN derivative models to enhance data sets [15].…”

Section: Gan Network Modelmentioning

confidence: 99%

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

Zhang

Liu

et al. 2019

Symmetry

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The symmetrical difference kernel SAR image edge detection algorithm based on the Canny operator can usually achieve effective edge detection of a single view image. When detecting a multi-view SAR image edge, it has the disadvantage of a low detection accuracy. An edge detection algorithm for a symmetric difference nuclear SAR image based on the GAN network model is proposed. Multi-view data of a symmetric difference nuclear SAR image are generated by the GAN network model. According to the results of multi-view data generation, an edge detection model for an arbitrary direction symmetric difference nuclear SAR image is constructed. A non-edge is eliminated by edge post-processing. The Hough transform is used to calculate the edge direction to realize the accurate detection of the edge of the SAR image. The experimental results show that the average classification accuracy of the proposed algorithm is 93.8%, 96.85% of the detection edges coincide with the correct edges, and 97.08% of the detection edges fall into the buffer of three pixel widths, whichshows that the proposed algorithm has a high accuracy of edge detection for kernel SAR images.

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“…Numerical methods via these calculi are developed in the literature, [5][6][7][8][9][10] sequence spaces are studied in Çakmak and Başar 11 and Tekin and Başar. 12 Multiplicative calculus is efficiently applied to image processing [13][14][15] and elasticity theory in economics. 16,17 The paper is organized in the following way.…”

Section: Introductionmentioning

confidence: 99%

On an alternative view to complex calculus

Bashirov

Norozpour

2018

Math Methods in App Sciences

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In most (if not all) textbooks on complex calculus, the differentiation and integration of complex functions are presented by using the algebraic form of complex variables because the respective formulae in terms of the polar form are inappropriate. In this paper, we demonstrate that by transferring the field structure of the system of complex numbers to the Riemann surface of complex logarithm and changing the sense of derivative and integral, complex calculus can be delivered in terms of the polar form of complex variables identically to the presentation in terms of algebraic form.

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Ultrasound Image Edge Detection Based on a Novel Multiplicative Gradient and Canny Operator

Cited by 26 publications

References 15 publications

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

Edge Detection Algorithm of a Symmetric Difference Kernel SAR Image Based on the GAN Network Model

On an alternative view to complex calculus

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