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%.

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

Agent Searching Edge Detection Algorithm for Bone Images

2012 ◽  
Vol 433-440 ◽  
pp. 6453-6456
Author(s):  
Hong Guang Zhang ◽  
Yuan’ An Liu ◽  
Bi Hua Tang ◽  
Zhi Peng Jia ◽  
Yan Qin
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Feature Vector ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Human Skeleton ◽  
Edge Detection Algorithm ◽  
Vector Correlation ◽  
Computer Aided

Bone image segmentation is the important technology for computer aided bone diagnosis system and the foundation for three-dimensional visualization of the human skeleton. Agent searching edge detection algorithm for bone images is proposed. Based on neighbor region correlation and regional harmonic mean feature vector correlation, different species of agent accomplish searching bone edge and experimental results are satisfactory. Experimental results comparison about the proposed algorithm, Prewitt, Sobel, Log and Canny is illustrated that demonstrates the proposed algorithm has advantages in some respects.

Automatic Exudates Detection in Diabetic Retinopathy Images

2016 ◽  
Vol 5 (2) ◽  
pp. 45
Author(s):  
H. Faouzi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Vascular Lesions ◽  
Edge Detection Algorithm ◽  
Invariant Moments ◽  
Recognition Phase ◽  
Prompt Diagnosis

Diabetic Retinopathy (DR) refers to the presence of typical retinal micro vascular lesions in persons with diabetics. When the disease is at the early state, a prompt diagnosis may help in preventing irreversible damages to the diabetic eye. If the exudates are closer to macula, then the situation is critical. Early detection can potentially reduce the risk of blind.  This paper proposes tool for the early detection of Diabetic Retinopathy using edge detection, algorithm kmeans in segmentation phase, invariant moments (Hu and Affine) and descriptor GIST in extraction phase. In the recognition phase, neural network is adopted. All tests are applied on database DIARETDB1.

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.

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