Paper Info
Title
Bimodal Model-Based 3d Vision And Defect Detection For Free-Form Surface Inspection
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.
Year
DOI
Venue
2017
10.5220/0006113304510458
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 4
Keywords
Field
DocType
Detection, Inspection, Free-form Surface, Photogrammetry, Photometric Stereo, Shape Analysis, Model-based, Data Simulation, Merging, Supervised Classification, Image Segmentation
Computer vision,Pattern recognition,Computer science,Artificial intelligence,Free form,3d vision
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Christophe Simler111.45
Dirk Berndt202.03
Christian Teutsch342.99