KEYWORDS: Shape analysis, Data modeling, Reflection, Data acquisition, Range imaging, Convolution, 3D image processing, Surface properties, Inspection, Image segmentation
Range imaging based on fast light-sectioning techniques is used to
acquire the three dimensional shape of a steel block with its
embedded flaws. The aim of the paper is the computation of
suitable features for describing the characteristics of
sequentially acquired profiles. The features should discriminate
well between flaws, the intact surface, and pseudo errors caused
by inhomogeneous surface properties such as, e.g., a strongly
changing reflection factor. The orthogonal distance from the
planar curve to its spline approximation serves as a suitable
descriptor. In addition, the multi-scale curvature is useful since
the defects are characterized as local perturbations of a
relatively smooth curve. Since the embedded flaws on the surface
of the steel block are existent in neighboring profiles, a measure
for evaluating the local perturbations with respect to subsequent
profiles is presented. Therefore, a kernel is used for weighting
the neighborhood of a defect. The shape of the kernel might be
Gaussian, however, a special kernel was developed for emphasizing
a preference direction of the flaws.
This paper presents a search-and-score approach for determining the network structure of Bayesian network classifiers. A selective unrestricted Bayesian network classifier is used which in combination with the search algorithm allows simultaneous feature selection and determination of the structure of the classifier.
The introduced search algorithm enables conditional exclusions of previously added attributes and/or arcs from the network classifier. Hence, this algorithm is able to correct the network structure by removing attributes and/or arcs between the nodes if they become superfluous at a later stage of the search. Classification results of selective unrestricted Bayesian network classifiers are compared to naive Bayes classifiers and tree augmented naive Bayes classifiers. Experiments on different data sets show that selective unrestricted Bayesian network classifiers achieve a better classification accuracy estimate in two domains compared to tree augmented naive Bayes classifiers, whereby in the remaining domains the performance is similar. However, the achieved network structure of selective unrestricted Bayesian network classifiers is simpler and computationally more efficient.
KEYWORDS: Defect detection, Range imaging, Detection and tracking algorithms, Reconstruction algorithms, Data modeling, Inspection, 3D modeling, Data acquisition, 3D image processing, Reflection
This paper shows an efficient and reliable method for the detection of surface defects with a three dimensional characteristic whereby the surface reflection properties are altering strongly. Due to this fact traditional intensity imaging techniques yield inferior performance. Therefore, light sectioning in conjunction with fast imaging sensors is applied to gather the range image of the steel block. Two different approaches for defect detection are treated, whereby the first algorithm is based on a line-wise examination of the acquired profiles by unwrapping the surface using spline interpolation. The noise in the unwrapped orthogonal distance may be reduced by applying statistical measures. The second method refers to surface segments and is based on the mean square error between the segment and its approximation gained from singular value decomposition. Due to vibrations the acquired profiles are arbitrary located within a range of a few millimeters which requires a geometric transformation to reconstruct the three dimensional surface of the steel block.
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