References of "Krim, Hamid"
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See detailSquigraphs for Fine and Compact Modeling of 3-D shapes
Aouada, Djamila UL; Krim, Hamid

in IEEE Transactions on Image Processing (2010), 19

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See detailMahalanobis-based Adaptive Nonlinear Dimension Reduction
Aouada, Djamila UL; Baryshnikov, Yuliy; Krim, Hamid

in 20th International Conference on Pattern Recognition (2010)

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See detailMeaningful 3D shape partitioning using Morse functions
Aouada, Djamila UL; Krim, Hamid

in 2009 16th IEEE International Conference on Image Processing (2009)

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See detailNovel similarity invariant for space curves using turning angles and its application to object recognition
Aouada, Djamila UL; Krim, Hamid

in IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009 (2009)

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See detail3D Object Recognition Using Fully Intrinsic Skeletal Graphs
Aouada, Djamila UL; Krim, Hamid

in International Conference on Computational Imaging, 2008 (2008, February)

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See detailGeometric modeling of rigid and non-rigid 3D shapes using the global geodesic function
Aouada, Djamila UL; Dreisigmeyer, David; Krim, Hamid

in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. (2008)

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See detail3D Mixed Invariant and its Application on Object Classification
Feng, Shuo; Aouada, Djamila UL; Krim, Hamid et al

in IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007 (2007, April)

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See detailStatistical Analysis of the Global Geodesic Function for 3D Object Classification
Aouada, Djamila UL; Feng, Shuo; Krim, Hamid

in IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007 (2007)

This paper presents a novel classification strategy for 3D objects. Our technique is based on using a global geodesic function to intrinsically describe the surface of an object. The choice of the global ... [more ▼]

This paper presents a novel classification strategy for 3D objects. Our technique is based on using a global geodesic function to intrinsically describe the surface of an object. The choice of the global geodesic function ensures the invariance of the classification procedure to scaling and all isometric transformations. Using the Jensen-Shannon divergence, feature parameters are extracted from the probability distribution functions of the global geodesic function for each one of the classes. These parameters are used in the decision of a class membership of an object. This approach demonstrates low computational cost, efficiency, and robustness to resolution over many different data sets. [less ▲]

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