Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
One-class svms for document classification
The Journal of Machine Learning Research
SMI '04 Proceedings of the Shape Modeling International 2004
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Efficient Computation of Isometry-Invariant Distances Between Surfaces
SIAM Journal on Scientific Computing
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Regularized Partial Matching of Rigid Shapes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A New Shape Benchmark for 3D Object Retrieval
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Robust principal axes determination for point-based shapes using least median of squares
Computer-Aided Design
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids
Computer-Aided Design
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
Shape analysis using the auto diffusion function
SGP '09 Proceedings of the Symposium on Geometry Processing
PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval
International Journal of Computer Vision
Computer Methods and Programs in Biomedicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
From 2D silhouettes to 3D object retrieval: contributions and benchmarking
Journal on Image and Video Processing
Affine-invariant diffusion geometry for the analysis of deformable 3D shapes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
SHREC'10 track: correspondence finding
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'10 track: non-rigid 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'11 track: shape retrieval on non-rigid 3D watertight meshes
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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The estimation of 3D surface correspondence constitutes a fundamental problem in shape matching and analysis applications. In the presence of non-rigid shape deformations, the ambiguity of surface correspondence increases together with the complexity of registration algorithms. In this paper, we alleviate this problem by means of 3D pose normalization using One-Class Support Vector Machines (OCSVM). In detail, we show how OCSVM are employed in order to increase the consistency of translation and scale normalization under articulations, extrusions or the presence of outliers. To estimate the relative translation and scale of an object, we use the 3D distribution of points that is modelled by employing OCSVM to estimate the decision surface corresponding to the surface points of the object under a preset tolerance to outliers. By discarding the outliers in the estimation of the object's center and size we compute the canonical pose of the core distribution that is less sensitive to intra-class shape variations. The effectiveness of the proposed method is demonstrated through the increased stability of translation and scale normalization and further justified by improving the precision of content-based 3D object retrieval.