Authors:
Christophe Simler
;
Dirk Berndt
and
Christian Teutsch
Affiliation:
Fraunhofer Institute for Factory Operation and Automation IFF, Germany
Keyword(s):
Detection, Inspection, Free-form Surface, Photogrammetry, Photometric Stereo, Shape Analysis, Model-based, Data Simulation, Merging, Supervised Classification, Image Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Segmentation and Grouping
Abstract:
This paper presents a 3D vision sensor and its algorithms aiming at automatically detect a large variety of
defects in the context of industrial surface inspection of free-form metallic pieces of cars. Photometric
stereo (surface normal vectors) and stereo vision (dense 3D point cloud) are combined in order to
respectively detect small and large defects. Free-form surfaces introduce natural edges which cannot be
discriminated from our defects. In order to handle this problem, a background subtraction via measurement
simulation (point cloud and normal vectors) from the CAD model of the object is suggested. This model-based
pre-processing consists in subtracting real and simulated data in order to build two complementary
“difference” images, one from photometric stereo and one from stereo vision, highlighting respectively
small and large defects. These images are processed in parallel by two algorithms, respectively optimized to
detect small and large defects and whose results
are merged. These algorithms use geometrical information
via image segmentation and geometrical filtering in a supervised classification scheme of regions.
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