A Numerical Solution to the Generalized Mapmaker's Problem: Flattening Nonconvex Polyhedral Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Mapping Using Surface Flattening via Multidimensional Scaling
IEEE Transactions on Visualization and Computer Graphics
Computational Surface Flattening: A Voxel-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
On interactive visualization of high-dimensional data using the hyperbolic plane
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Applied Multidimensional Scaling
Applied Multidimensional Scaling
Three-Dimensional Face Recognition
International Journal of Computer Vision
Geometric modeling in shape space
ACM SIGGRAPH 2007 papers
Sampling and Reconstruction of Surfaces and Higher Dimensional Manifolds
Journal of Mathematical Imaging and Vision
Analysis of Two-Dimensional Non-Rigid Shapes
International Journal of Computer Vision
Local versus Global in Quasi-Conformal Mapping for Medical Imaging
Journal of Mathematical Imaging and Vision
Geometric sampling of manifolds for image representation and processing
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Robust expression-invariant face recognition from partially missing data
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Isometric Embeddings in Imaging and Vision: Facts and Fiction
Journal of Mathematical Imaging and Vision
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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The problem of isometry-invariant representation and comparison of surfaces is of cardinal importance in pattern recognition applications dealing with deformable objects. Particularly, in three-dimensional face recognition treating facial expressions as isometries of the facial surface allows to perform robust recognition insensitive to expressions. Isometry-invariant representation of surfaces can be constructed by isometrically embedding them into some convenient space, and carrying out the comparison in that space. Presented here is a discussion on isometric embedding into $\mathbb{S}^{\rm 3}$, which appears to be superior over the previously used Euclidean space in sense of the representation accuracy.