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Automatic Surface Reconstruction Based on the Fusion of Fuzzy Logic and Robust Estimation Techniques
Author(s) -
Samadzadegan Farhad,
Azizi Ali,
Lucas Caro,
Hahn Michael
Publication year - 2002
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/0031-868x.00212
Subject(s) - mathematics , matching (statistics) , similarity (geometry) , object (grammar) , fuzzy logic , image (mathematics) , surface (topology) , point (geometry) , artificial intelligence , conjugate points , algorithm , computer science , geometry , statistics
Automatic surface reconstruction by means of digital image matching essentially involves computation in both image and object spaces. In image space, image matching is performed according to certain radiometric and geometric similarity criteria for conjugate point determination. The matching operations may therefore be regarded as decision–making processes. In practice, however, the similarity criteria for conjugate point determination demonstrate non–deterministic behaviour. Thus, the nature of these decision–making processes is more compatible with fuzzy reasoning concepts. In object space, on the other hand, surface reconstruction procedures, which operate on the basis of previously determined conjugate points, are essentially deterministic in nature and hence demand rigorous geometric modelling. In this paper, a new approach for automatic surface modelling is proposed, based on two different mathematical treatments: a fuzzy logic reasoning method for the conjugate point determination in image space; and a robust finite elements approach for the surface modelling in object space. Tests carried out on real data demonstrate the high potential of the strategy proposed.