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.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

The Implementation of Infrared Image Edge Detection Algorithm Based on CNN on FPGA

2012 ◽  
Vol 41 (11) ◽  
pp. 1354-1358 ◽  
Author(s):  
王巍 WANG Wei ◽  
安友伟 AN You-wei ◽  
黄展 HUANG Zhan ◽  
丁锋 DING Feng ◽  
杨铿 YANG Keng ◽  
...  
Keyword(s):  
Edge Detection ◽  
Infrared Image ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Image Edge Detection ◽  
Image Edge

An Improved Canny Edge Detection Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2869-2873 ◽  
Author(s):  
Tao Sun ◽  
Chang Zhi Gao
Keyword(s):  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Threshold Method ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Global Threshold ◽  
Canny Edge ◽  
Canny Edge Detection Algorithm

Traditional Canny edge detection algorithm uses a global threshold selection method, when large changes are in the background of the image and the target gray, global threshold method may lose some local edge information. For this problem, this paper therefore proposes an adaptive dynamic threshold improved Canny edge detection algorithm. The method uses image gradient variance as the criterion of the image block according to the four forks tree principle, then uses the Otsu method to get the corresponding sub-block threshold value for each sub-block, and obtains threshold value matrix by interpolation, finally, gets image edge with improved edge connected algorithm. Experimental results show that, the algorithm not only has good anti-noise performance, but also better detection accuracy.

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.

An Adaptive Edge Detection Method Based on Canny Operator

2011 ◽  
Vol 255-260 ◽  
pp. 2037-2041
Author(s):  
Bai He Lang ◽  
Ling Yun Shen ◽  
Tai Lin Han ◽  
Yu Qun Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Canny Operator ◽  
Edge Detection Algorithm ◽  
Sobel Operator ◽  
Otsu Method ◽  
Edge Detection Method ◽  
Roberts Operator

This paper proposes an adaptive Canny operator edge detection algorithm. The proposed method can automatically set the threshold value according to the different image gray-scale gradient histogram adaptively and improve the performance in the detail edge detection and good localization. Experiments show that this method produces better edge detection results performance than the Otsu method. Besides our method, Roberts operator, Prewitt operator, Sobel operator, Log operator and Canny operator based on Otsu algorithm are also tested for comparisons.

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.

Sign in / Sign up

Export Citation Format

Share Document