Symbolic signatures for deformable shapes

Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly diffi...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 28; H. 1; S. 75 - 90
Hauptverfasser: Ruiz-Correa, S., Shapiro, L.G., Meila, M., Berson, G., Cunningham, M.L., Sze, R.W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Los Alamitos, CA IEEE 01.01.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Application software
Applied sciences
Artificial Intelligence
Biometry - methods
Computer science; control theory; systems
Computer vision
craniofacial malformations
craniosynostosis
deformable shapes
Deformation
Exact sciences and technology
Face - anatomy & histology
Formability
Humans
Image analysis
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Index Terms- Three-dimensional object recognition and classification
Layout
Machine vision
Medical diagnosis
Mercer kernel
numeric and symbolic signatures
Object recognition
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Photogrammetry - methods
Photography - methods
range data
Recognition
Representations
Reproducibility of Results
Robot vision systems
Robustness
scene analysis
Sensing devices
Sensitivity and Specificity
Shape
Shape recognition
Subtraction Technique
Three dimensional
Computer vision
Mercer kernel
Image processing
Craniosynostosis
Three dimensional shape
Digital signal
numeric and symbolic signatures
craniofacial malformations
deformable shapes
Scene analysis
Tridimensional image
Pattern analysis
range data
Medical application
Three-dimensional object recognition and classification
ISSN:0162-8828, 1939-3539
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Beschreibung
Zusammenfassung:Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2006.23