In this paper we introduce a novel algorithm for automatic fault detection in textures. We study the problem of
finding a defect in regularly textured images with an approach based on a template matching principle.
We aim at registering patches of an input image in a defect-free reference sample according to some admissible
transformations. This approach becomes feasible by introducing the so-called discrepancy norm as fitness function
which shows particular behavior like a monotonicity and a Lipschitz property. The proposed approach relies
only on few parameters which makes it an easily adaptable algorithm for industrial applications and, above all,
it avoids complex tuning of configuration parameters.
Experiments demonstrate the feasibility and the reliability of the proposed algorithms with textures from
real-world applications in the context of quality inspection of woven textiles.
In this paper it is demonstrated, how research in optical coherence tomography (OCT) for biomedical diagnostics
successfully triggered new developments in the field of mechanical material testing. With the help of a specifically
designed, compact and robust spectral domain polarization sensitive OCT (SD-PS-OCT) setup, which is operating at
1.55 μm, dynamic investigations of technical materials - like bulk polymers and composite samples - can be performed
under various conditions. Already by evaluating the speckle pattern of the standard SD-OCT images with advanced
image processing methods, valuable information on the deformation and flow characteristics of samples subjected to
tensile tests can be obtained. By additionally taking the birefringence properties into account, complementary knowledge
on the evolvement of the internal stress situation is obtained in a spatially resolved way.
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