scholarly journals Edge detection on variance of gray level.

1989 ◽  
Vol 7 (5) ◽  
pp. 453-463
Author(s):  
Kozo FUJIMOTO ◽  
Satoru KIMURA ◽  
Toshiki HINO ◽  
Shuji NAKATA
Keyword(s):  
Edge Detection ◽  
Gray Level

scholarly journals Edge Detection in Gray Level Images based on the Shannon Entropy

2008 ◽  
Vol 4 (3) ◽  
pp. 186-191 ◽  
Author(s):  
Baljit Singh ◽  
Amar Partap Singh
Keyword(s):  
Edge Detection ◽  
Shannon Entropy ◽  
Gray Level ◽  
Gray Level Images

A Sub-Pixel Edge Detection Method Combining the Interpolation with the Moment Theory

2013 ◽  
Vol 475-476 ◽  
pp. 351-354
Author(s):  
Ya Zhou Zhou ◽  
Qiu Cheng Sun ◽  
Hao Chen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
Detection Accuracy ◽  
Gray Level ◽  
Two Dimensional ◽  
Moment Theory ◽  
One Dimensional ◽  
Edge Detection Method ◽  
The Moment

A new sub-pixel edge detection method is proposed to improve the detection accuracy. Firstly, using the theory of interpolation to acquire the continuous gray level distribution in one-dimensional .Therefore, the location of edge is determined. Secondly, in view of the two-dimensional edge detection, the moment spatial is taken into account. At last, the two-dimensional edge detection simplified as one-dimensional. From the test ,its known that the accuracy of the this algorithm is higher, especially for images with noise. So, the proposed algorithm has good applicability in image processing.

Edge Detection Combined Local Gray Level Difference and Competitive Fuzzy Logic

2012 ◽  
Vol 220-223 ◽  
pp. 1284-1287
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Experimental Result ◽  
Gray Level ◽  
Human Visual Perception ◽  
Edge Image ◽  
New Approach ◽  
Level Difference ◽  
Edge Detection Method ◽  
Noisy Condition

In this paper, we present a new approach by local gray level difference based competitive fuzzy edge detection. In the light of human visual perception, a preprocessing step is proposed to simplify original images and further enhance the performance of edge extraction. Then we define the feature vector of each pixel in four directions and six edge prototype. Finally, BP neural network is used to classify the type of edge, and the competitive rule is adopted to thin the thick edge image. From the experimental result, it can be seen that the edge detection method proposed in this paper is superior to Canny method and Log method under the noisy condition.

The Application of MATLAB in Welding Image Processing

2012 ◽  
Vol 201-202 ◽  
pp. 300-303 ◽  
Author(s):  
Yuan Peng Liu ◽  
Xin Sun ◽  
Zhen Hua Wen
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Methods ◽  
Gray Level ◽  
The Real ◽  
Edge Points ◽  
Welding Image ◽  
Margin Detection ◽  
Maximal Points

Firstly the margin-detection methods commonly used are presented in this paper. The algorithm idea is that the edge points correspond to the local maximal points of original image’s gray-level gradient. These algorithms are very sensitive to noises if these images mixed much noise, which usually leads to wrongly detect noise points as marginal points, and the real edge can not be detected as the interference action of noise. However, we perform kinds of pretreatments on these images under the MATLAB environment and adequately make use of the functions of image processing toolbox to satisfy the need of edge detection. Lastly, Combined with practical examples, the specific application of MATLAB in edge detection is analyzed in detail.

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.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

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