Inferring surface trace and differential structure from 3-D images

Early image understanding seeks to derive analytic representations from image intensities. The authors present steps towards this goal by considering the inference of surfaces from three-dimensional images. Only smooth surfaces are considered and the focus is on the coupled problems of inferring the...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 12; no. 9; pp. 833 - 854
Main Authors Sander, P.T., Zucker, S.W.
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.09.1990
IEEE Computer Society
Subjects
Algorithm design and analysis
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Degradation
Exact sciences and technology
Image analysis
Iterative algorithms
Magnetic noise
Magnetic resonance
Minimization methods
Parameter estimation
Pattern recognition. Digital image processing. Computational geometry
Robust stability
Stability analysis
Parametrization
Computer vision
Image analysis
Parameter estimation
Three dimensional representation
Differential geometry
Minimization
Three dimensional space
Curved surface
Constrained optimization
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Summary:Early image understanding seeks to derive analytic representations from image intensities. The authors present steps towards this goal by considering the inference of surfaces from three-dimensional images. Only smooth surfaces are considered and the focus is on the coupled problems of inferring the trace points (the points through which the surface passes) and estimating the associated differential structure given by the principal curvature and direction fields over the estimated smooth surfaces. Computation of these fields is based on determining an atlas of local charts or parameterizations at estimated surface points. Algorithm robustness and the stability of results are essential for analyzing real images; to this end, the authors present a functional minimization algorithm utilizing overlapping local charts to refine surface points and curvature estimates, and develop an implementation as an iterative constraint satisfaction procedure based on local surface smoothness properties. Examples of the recovery of local structure are presented for synthetic images degraded by noise and for clinical magnetic resonance images.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0162-8828
1939-3539
DOI:10.1109/34.57680