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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 28; číslo 1; s. 75 - 90
Hlavní autoři:	Ruiz-Correa, S., Shapiro, L.G., Meila, M., Berson, G., Cunningham, M.L., Sze, R.W.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.01.2006 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2006.23