Remote sensing image edge-detection based on improved Canny operator

2016 ◽  
Author(s):  
Shi Guiming ◽  
Suo Jidong
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Remote Sensing Image ◽  
Canny Operator ◽  
Image Edge Detection ◽  
Image Edge

An Improved Edge Detection Algorithm Based on Canny Operator

2013 ◽  
Vol 347-350 ◽  
pp. 3541-3545 ◽  
Author(s):  
Dan Dan Zhang ◽  
Shuang Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Image Simulation ◽  
Edge Image ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Edge Detection Algorithm

The traditional Canny edge detection algorithm is analyzed in this paper. To overcome the difficulty of threshold selecting in Canny algorithm, an improved method based on Otsu algorithm and mathematical morphology is proposed to choose the threshold adaptively and simultaneously. This method applies the improved Canny operator and the image morphology separately to image edge detection, and then performs image fusion of the two results using the wavelet fusion technology to obtain the final edge-image. Simulation results show that the proposed algorithm has better anti-noise ability and effectively enhances the accuracy of image edge detection.

A Novel Edge Detection Algorithm for Medical Image

2012 ◽  
Vol 151 ◽  
pp. 653-656
Author(s):  
Zhan Chun Ma ◽  
Xiao Mei Ning
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Detection Algorithm ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Traditional Algorithm ◽  
Image Edge ◽  
Improved Algorithm

CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.

scholarly journals Quaternion-Based Improved Artificial Bee Colony Algorithm for Color Remote Sensing Image Edge Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Dujin Liu ◽  
Huajun Wang ◽  
Sen Wang ◽  
Guolin Pu ◽  
Xiaoya Deng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Artificial Bee Colony ◽  
Color Image ◽  
Image Data ◽  
Remote Sensing Image ◽  
Bee Colony ◽  
Image Edge ◽  
Image Details ◽  
Detection Effect

As the color remote sensing image has the most notable features such as huge amount of data, rich image details, and the containing of too much noise, the edge detection becomes a grave challenge in processing of remote sensing image data. To explore a possible solution to the urgent problem, in this paper, we first introduced the quaternion into the representation of color image. In this way, a color can be represented and analyzed as a single entity. Then a novel artificial bee colony method named improved artificial bee colony which can improve the performance of conventional artificial bee colony was proposed. In this method, in order to balance the exploration and the exploitation, two new search equations were presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, some more reasonable artificial bee colony parameters were proposed to improve the performance of the artificial bee colony. Then we applied the proposed method to the quaternion vectors to perform the edge detection of color remote sensing image. Experimental results show that our method can get a better edge detection effect than other methods.

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

2014 ◽  
Vol 37 (3) ◽  
pp. 238-250 ◽  
Author(s):  
Yinfei Zheng ◽  
Yali Zhou ◽  
Hao Zhou ◽  
Xiaohong Gong
Keyword(s):  
Edge Detection ◽  
Ultrasound Image ◽  
Canny Operator ◽  
Image Edge Detection ◽  
Image Edge

scholarly journals Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ming Chen
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fractional Order ◽  
Remote Sensing Images ◽  
Detection Model ◽  
Image Edge Detection ◽  
Texture Information ◽  
Image Edge ◽  
Fidelity Term ◽  
Model Algorithm

In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

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