Edge Detection Algorithm for Surface Image Based on Euclidean Distance Discriminant

2012 ◽  
Vol 459 ◽  
pp. 347-350
Author(s):  
Gang Li ◽  
Zi Qiang Li ◽  
Ya La Tong
Keyword(s):  
Edge Detection ◽  
Euclidean Distance ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Surface Image

Edge Detection Algorithm for Surface Image Based on Euclidean Distance Discriminant

2012 ◽  
Vol 459 ◽  
pp. 347-350 ◽  
Author(s):  
Gang Li ◽  
Zi Qiang Li ◽  
Ya La Tong
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Euclidean Distance ◽  
Detection Algorithm ◽  
Target Information ◽  
Edge Detection Algorithm ◽  
Background Interference ◽  
Surface Image

For the problem of difficult to detect the edge in the surface image processing, we used the Euclidean distance from the selected pixels in the corner of the neighborhood window to the centre point of that to measure the extent of being edge, and then search for suitable threshold to extract the expected edge pixels. This algorithm can extract target information better, while also inhibiting the background interference, and it is a good algorithm which is worthy of further exploration

scholarly journals Iris Recognition Using Image Moments and k-Means Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yaser Daanial Khan ◽  
Sher Afzal Khan ◽  
Farooq Ahmad ◽  
Saeed Islam
Keyword(s):  
Edge Detection ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Feature Vector ◽  
Detection Algorithm ◽  
Grayscale Image ◽  
Translation Invariant ◽  
Edge Detection Algorithm ◽  
Invariant Moments ◽  
Rectangular Form

This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

Application of Improved Canny Edge Detection Algorithm in Coal-Rock Interface Recognition

2012 ◽  
Vol 220-223 ◽  
pp. 1279-1283 ◽  
Author(s):  
Li Hong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Rock Interface ◽  
Coal Rock ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

The coal-rock interface recognition is one of the critical automated technologies in the fully mechanized mining face. The poor working conditions underground result in the seriously polluted edge information of the coal-rock interface, which affects the positioning precision of the shearer drum. The Gaussian filter parameters and the high-low thresholds are difficult to select in the traditional Canny algorithm, which causes the information loss of gradual edge and the phenomenon of false edge. Consequently, this paper presents an improved Canny edge detection algorithm, which adopts the adaptive median filtering algorithm to calculate the thresholds of Canny algorithm according to the grayscale mean and variance mean. This algorithm can protect the image edge details better and can restrain the blurred image edge. Experimental results show that this algorithm has improved the edge extraction effect under the case of noise interference and improved the detection precision and accuracy of the coal-rock image effectively.

Surface Image Edge Detection Algorithm Based on Grey Relational Analysis

2012 ◽  
Vol 461 ◽  
pp. 343-346 ◽  
Author(s):  
Gang Li ◽  
Ying Fang ◽  
Ya La Tong
Keyword(s):  
Edge Detection ◽  
Good Method ◽  
Detection Algorithm ◽  
Relational Model ◽  
Small Data ◽  
The Road ◽  
Image Edge Detection ◽  
Surface Image ◽  
Image Edge ◽  
Grey Relational

Automatic detection of pavement cracks is one of the very hot topics. For the characteristics of “small data, poor information” in the surface image processing, we construct ed a grey image relational model to characterize the local image edge feature, by selecting the appropriate threshold to extract the edge of appropriate level. Finally, simulation experiments show that the new algorithm can effectively improve the road edge detection results, and it is an effective good method worthy further study.

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