Paper Title:
Precise Vectorization of Industrial Computed Tomographic Image
  Abstract

Circle, line and circular arc are the common basic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

  Info
Periodical
Advanced Materials Research (Volumes 156-157)
Edited by
Jingtao Han, Zhengyi Jiang and Sihai Jiao
Pages
523-528
DOI
10.4028/www.scientific.net/AMR.156-157.523
Citation
B. He, F. L. Liu, B. Bi, "Precise Vectorization of Industrial Computed Tomographic Image", Advanced Materials Research, Vols. 156-157, pp. 523-528, 2011
Online since
October 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Ji Gang Wu, Kuan Fang He, Bin Qin
Abstract:Aiming at the subpixle edge detection of speckle in autofocus for micro-machine vision, a novel accurate subpixel edge detection algorithm...
228
Authors: Wei Ke Liu, Gou Lin Liu, Xiao Qing Zhang
Chapter 8: Measurement
Abstract:The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the...
1876
Authors: Ravinder Kumar, Pravin Chandra, M. Hanmandlu
Chapter 7: Machining
Abstract:This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on...
888
Authors: Xue Feng Wu, Yu Fan
Chapter 6: Mechatronics
Abstract:A new algorithms for parameters of an image irregular boundary circle parameters is presented, which is based on “Curve-Approximate Method”...
639
Authors: Fang Jie Yu, Xin Luan, Da Lei Song, Xiu Fang Li, Hong Hong Zhou
Chapter 7: Other Measurement Methods and Its Application
Abstract:This paper presents a novel sub-pixel corner detection algorithm for camera calibration. In order to achieve high accuracy and robust...
713