TY  - GEN
N2  - X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.
ID  - heidok3106
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/3106/
Y1  - 2002///
TI  - Automated defect detection and evaluation in X-ray CT images
KW  - X-ray Imaging 
KW  -  Computerized Tomography 
KW  -  Image Processing 
KW  -  Defect Detection
A1  - Eisele, Heiko
AV  - public
ER  -