Nonlinear image decomposition for multiresolution edge detection using gray-level edge maps

2002 ◽  
Vol 41 (11) ◽  
pp. 2749 ◽  
Author(s):  
Junaid I. Siddique
Keyword(s):  
Edge Detection ◽  
Image Decomposition ◽  
Gray Level ◽  
Multiresolution Edge Detection

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

Multiresolution edge detection based on modified bubble function and SWT

2006 ◽  
Vol 2 (1) ◽  
pp. 72-74 ◽  
Author(s):  
Rui-xing Yu ◽  
Yan-jun Li ◽  
Ke Zhang
Keyword(s):  
Edge Detection ◽  
Bubble Function ◽  
Multiresolution Edge Detection

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.

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