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.

Three-dimensional contour edge detection algorithm

2000 ◽  
Author(s):  
Yizhou Wang ◽  
Sim Heng Ong ◽  
Ashraf A. Kassim ◽  
Kelvin W. C. Foong
Keyword(s):  
Edge Detection ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Edge Detection Algorithm

Airport Runway Debris Edge Detection Algorithm

2014 ◽  
Vol 1065-1069 ◽  
pp. 671-674
Author(s):  
Yan Ying Guo ◽  
Zhi Gang Liu
Keyword(s):  
Edge Detection ◽  
Environmental Conditions ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Edge Detection Algorithm ◽  
Image Edge ◽  
Adverse Environmental Conditions ◽  
Airport Runway ◽  
Morphology Algorithm ◽  
Image Details

Airport runway debris detection decides security of plane and passengers. The relative merits of the various types of sensor and the effects of adverse environmental conditions on their performance, a novel fuzzy weighted morphology algorithm of detection about runway debris is approved. The experimental results show that the proposed algorithm, compared with a few classic algorithms of edge detection, which can achieves the image edge precisely and reduce noises, better retain the image details, and adaptively detect the complete and successive edges.

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.

Research on Edge Detection Algorithm for Caption Extraction from Video Frames

2012 ◽  
Vol 190-191 ◽  
pp. 1040-1043
Author(s):  
Jin Wu
Keyword(s):  
Edge Detection ◽  
Test Object ◽  
Video Retrieval ◽  
Detection Algorithm ◽  
Video Data ◽  
Experimental Results ◽  
Video Frame ◽  
Edge Detection Algorithm ◽  
Video Frames ◽  
Caption Text

Text area extraction from video caption has become an important tool for content-based video retrieval. Test object is frame data intercepted from video data. This paper proposes an algorithm of edge detection to extract caption text that embedded in the video frame by grayed processing. Experimental results have shown that the proposed approach is very effective in text area detection.

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%.

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