Dimensionality-Reduction Using Connectionist Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Neural networks and the bias/variance dilemma
Neural Computation
Active shape models—their training and application
Computer Vision and Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flexible images: matching and recognition using learned deformations
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualizing and Modeling Scattered Multivariate Data
IEEE Computer Graphics and Applications
Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Multimodal Data Representations with Parameterized Local Structures
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Nonlinear Shape Statistics via Kernel Spaces
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Shape variability and spatial relationships modeling in statistical pattern recognition
Pattern Recognition Letters
Auto-associative models and generalized principal component analysis
Journal of Multivariate Analysis
Improved facial-feature detection for AVSP via unsupervised clustering and discriminant analysis
EURASIP Journal on Applied Signal Processing
Distribution-based similarity measures for multi-dimensional point set retrieval applications
MM '08 Proceedings of the 16th ACM international conference on Multimedia
An Elasticity Approach to Principal Modes of Shape Variation
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
An Elasticity-Based Covariance Analysis of Shapes
International Journal of Computer Vision
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA
Pattern Recognition Letters
Neural Processing Letters
| Hi-index | 0.14 |
We are given a set of points in a space of high dimension. For instance, this set may represent many visual appearances of an object, a face, or a hand. We address the problem of approximating this set by a manifold in order to have a compact representation of the object appearance. When the scattering of this set is approximately an ellipsoid, then the problem has a well-known solution given by Principal Components Analysis (PCA). However, in some situations like object displacement learning or face learning, this linear technique may be ill-adapted and nonlinear approximation has to be introduced. The method we propose can be seen as a Non Linear PCA (NLPCA), the main difficulty being that the data are not ordered. We propose an index which favors the choice of axes preserving the closest point neighborhoods. These axes determine an order for visiting all the points when smoothing. Finally, a new criterion, called "generalization error," is introduced to determine the smoothing rate, that is, the knot number for the spline fitting. Experimental results conclude this paper: The method is tested on artificial data and on two data bases used in visual learning.