Research On High Accuracy Measurement Method Of Industrial CT Image

As rapid development of technology and industrial production, the geometrical parameters measurement of 3D object is proposed by many fields. Recently, when the CT is used, the transfer from test to measurement is also a main research direction. CT image measurement is a method that measures the parameter through processing CT slices, and it is used in many fields, such as industrial testing and medicine examining. Applications ask for higher and higher accuracy of measurement, but the accuracy of widely used methods are not high enough. So the measurement method of higher accuracy must be proposed by request. The main research contents and contribution in the thesis are as follows:In the applications such as image measurement, the object's edge information of high accuracy is required. First, a sub-pixel edge detection method based on Facet model is introduced, the method can reduce noise and achieve high accuracy, but its computational complexity is too high. Aimed at making up this disadvantage, we studied an improved method, which combined the 2D Facet model and Mallat's maximum wavelet module approach effectively. The wavelet method is used to extract wide preparatory edge, while restrain some noise. Then the method based on 2D Facet model merely processes preparatory edge points and further obtains sub-pixel edge. The improved method not only increases the speed, but also provides more continue accurate edge and reduces noise.In order to improve the accuracy of 2D image measurement, a sub-pixel measurement method was studied, and applied in actual industrial CT images. Firstly, the improved sub-pixel edge detection method is used to extract the sub-pixel edges. This method can achieve higher accuracy, reduce noise, and offer accurate data for further area measurement. Secondly, the method of minimizing distance search is studied to separate and sort the edge of the measured object. This search method can separate the edge points of the measured object from the floating-point and discontinuous edges of whole image, obtain sorted edge points chain, and offer available data for the next computing. At last, Green formula and Euclid distance formula are adopted to compute the 2D parameters.In order to improve the accuracy and automatism of 3D measurement, a sub-voxel measurement method was studied, and applied in actual industrial CT voxel data. Firstly, an edge detection method based on 3D Facet model is used to extract the sub-voxel edges. This method considers the information of slices adjacent to the being processed one, can reduce more noises and achieve higher accuracy when compared with the 2D sub-pixel edge detectors which only use single slice information. Secondly, the method of reducing dimension and minimizing distance search are studied to separate and sort the edge of the measured object. At last, the surface of the object is reconstructed by many small triangles using shortest diagonal method, and the three 3D can be compute through these triangles.This thesis has studied on industrial CT image measurement. Based on anlysising disadvantages of prevenient measurement methods, we present the measurement methods of higher accuracy. The experimentations have done on emulational images and actual industrial CT images, and the results show the validity of the method. The measurement methods in this paper are based on sub-pixel (sub-voxel) edge, which breaks through the restriction of measurement precision by image resolution.