An Edge Detection Algorithm Based on Edge-Preserving

2016 ◽  
Vol 693 ◽  
pp. 1321-1325
Author(s):  
C.R. Tang ◽  
A. Li
Keyword(s):  
Edge Detection ◽  
Gaussian Noise ◽  
Detection Algorithm ◽  
Order Differential Operator ◽  
Boundary Extraction ◽  
Edge Preserving ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
First Order ◽  
Simulation Results

The traditional first-order differential operator is under the influence of the Gaussian noise, therefore, it often implement boundary extraction after average filtering. But the filtering process would often smooth the details of some directions of image too much, so that the edge cannot be extracted correctly. To solve this problem, this paper puts forward the edge detection algorithm based on edges keep, to determine the keeping direction of the edge through matching different directions’ edge template. Instead of average filtering process, it can improve the performance of traditional operator, and provide the simulation results. Experimental results show that the algorithm can eliminate noise, and at the same time, keep more edge information of the image.

A Novel Extracting Algorithm of the Low Gray Fuzzy Edges for Infrared Images

2014 ◽  
Vol 1037 ◽  
pp. 411-415
Author(s):  
Dong Xing Li ◽  
Liang Geng ◽  
Qin Jun Du ◽  
Han Ren ◽  
Ai Jun Li ◽  
...  
Keyword(s):  
Edge Detection ◽  
Infrared Image ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Conversion Function ◽  
Gray Level ◽  
Infrared Images ◽  
Computation Complexity ◽  
Edge Information ◽  
Edge Detection Algorithm

The fuzzy edge detection algorithm proposed by Pal-King has some disadvantages for extracting the low gray level edge information for the infrared images, such as high computation complexity, low threshold segmentation inaccuracy and the leakage edge information. For overcoming the disadvantages, the improved image fuzzy edge detection algorithm is proposed in this paper. First, redefining membership function to simplify computation complexity, the new conversion function enable the function transform interval is [0, 1], thus the value of the low gray level edge is not to be set to 0. Second, Ostu's algorithm is used in the selection of segmentation threshold named as transit point. The traditional threshold value is improved in order to make the segmentation accurate. The experimental results show that the lower gray infrared image edge information is preserved via proposed algorithm in this paper. The detecting results are more accurate. The run time is decreased obviously than the traditional Pal - king algorithm.

Application of Bidimensional Empirical Mode Decomposition to Medical Liquid Opacity Detection

2011 ◽  
Vol 128-129 ◽  
pp. 530-533
Author(s):  
Jian Wan ◽  
Yuan Peng Diao ◽  
Dong Mei Yan ◽  
Qiang Guo ◽  
Zhen Shen Qu
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
External Environment ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Edge Detection Algorithm ◽  
Mode Decomposition ◽  
Simulation Results ◽  
Bidimensional Empirical Mode Decomposition

A Robert operator edge detection algorithm based on Bidimensional Empirical Mode Decomposition (BEMD) to detect medical liquid opacity is proposed. This method can effectively resolve the problem that traditional Robert operator edge detection can be easily effected by noise, and it also has certain effects on restraining external environment influence. The simulation results show that, compare with traditional medical liquid opacity detection methods, the proposed method could achieve higher detection accuracy, and has a certain theory and application value.

scholarly journals Texture Construction Edge Detection Algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 897 ◽  
Author(s):  
Shou-Cih Chen ◽  
Chung-Cheng Chiu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
Detection Technology ◽  
Texture Change ◽  
Object Edge ◽  
Edge Detection Method ◽  
Threshold Setting

The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.

Research on Broken Filament Detection Method of Silk Product

2014 ◽  
Vol 716-717 ◽  
pp. 848-850
Author(s):  
Chang Niu Yang ◽  
Xing Bo Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Gabor Filter ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
Ideal Image ◽  
Image Edge ◽  
Image Edges

In this paper, we put forward a kind of adaptive edge detection algorithm of Gabor filter for silk product broken filament image. Use different directions’ Gabor filter to respectively get the broken filament image edge information. Using the method proposed in this paper to fuse the edges adaptively obtained from different directions Gabor filter, we obtain ideal image edges, and effectively eliminate the noise, also enhance the fuzzy edges at the same time. Experimental results show that the algorithm for silk products processing is effective, and the broken filament detected is clear.

Application of Multi-Scale Morphology Gradient in the Foreign Fibers Recognition

2011 ◽  
Vol 411 ◽  
pp. 514-517
Author(s):  
Shou Feng Jin ◽  
Jian Chang Yuan
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Scale Morphology ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
Key Technology ◽  
Multi Scale ◽  
Structure Morphology ◽  
Traditional Algorithm ◽  
The Ideal

The extraction of edge feature is a key technology in the foreign fibers recognition. The traditional algorithm of edge detection operator is more sensitive to noise, which is unable to extract complete edge information. Based on the morphological edge detection, we can get the ideal foreign fibers edge characteristics under the condition of noise for the multi-scale structure elements, combining with inflation and corrosion operator to structure morphology gradient operator, the adjustment of morphology structure elements dimensions. Experiment results show that it has a certain feasibility and effectiveness compared to classic edge detection algorithm in the respect of restraining the noise and improving the testing precision.

Research on Jumper and Connector Detection of Silk Products

2014 ◽  
Vol 716-717 ◽  
pp. 851-853
Author(s):  
Chang Niu Yang ◽  
Xing Bo Sun
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Fusion Method ◽  
Edge Information ◽  
Edge Detection Algorithm ◽  
Ideal Image ◽  
Histogram Matching ◽  
Adaptive Fusion ◽  
Image Edge

An improved morphological edge detection algorithm for silk products jumpers and connectors’ test was proposed. With structure elements of different models, we detect the edge information in different directions of silk products respectively; using the proposed adaptive fusion method based on histogram matching, we can obtain ideal image edge, while enhance the blurred edges, and effectively eliminate the silk products inherent texture and noise, then detect the clear jumpers and connectors.

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