Active shape models—their training and application
Computer Vision and Image Understanding
Machine Learning
Shape and the information in medical images: a decade of the morphometric synthesis
Computer Vision and Image Understanding
Characterization of Neuropathological Shape Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Control of polygonal mesh resolution for 3-D computer vision
Graphical Models and Image Processing
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
A multiresolution framework for dynamic deformations
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM Transactions on Graphics (TOG)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Estimating the tensor of curvature of a surface from a polyhedral approximation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Statistical shape analysis of anatomical structures
Statistical shape analysis of anatomical structures
The Journal of Machine Learning Research
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Reflective Symmetry Descriptor for 3D Models
Algorithmica
Estimating the Support of a High-Dimensional Distribution
Neural Computation
A salient-point signature for 3d object retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A Learning Approach to 3D Object Representation for Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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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.