For fast inspection of defects in textile fabric the complexity of calculations has to be reduced, in order to limit the system costs. Additionally, algorithms which are suitable for migration into hardware have to be chosen. Therefore, in this work a segmentation algorithm using first order statistics is applied. Preprocessing includes a logarithmic greyscale intransformation to obtain insensitivity to illumination changes. Afterwards texture features are extracted by a set of linear filters, which consider local neighbourhood relations. The filtered images are evaluated by histograms being calculated on a window grid. Finally, the histograms are classified by a Perceptron Net trained by Backpropagation. An interactive Teach-in program is provided to adapt the system to different kinds of textile fabric and appearances of defects
A new algorithm for 3D-reconstruction of objects from x-ray projections, called Planar Computer Tomography (PCT), is applied to inspection of solder joints. In contrast to axial computer tomography reconstruction is based on linear object movements instead of axial rotation. By this the handling of the Printed Circuit Board is simplified and integration into the conventional x-ray inspection process is enabled. Since the algorithm is well suited for parallel computers and only a few projections are necessary a significant speed up will be achieved compared with axial Computer Tomography (CT) Computational complexity of CT is another limiting factor for application in electronic production. This problem is tackled by a reduction of the number of projections, by integration of model based knowledge and by using highly parallel computers. In the following main aspects of the new reconstruction algorithm are summarized: -Reconstruction is resticted to small volumes in suspect areas of interest (A01) Multiresolution image processing Integration of model based knowledge Algebraic Reconstruction Technique (ART) --and other 3D inspection techniques.
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