Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Modeling and Visualization of the Flexibility in Morphable Models
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Tracking Hand Rotation and Grasping from an IR Camera Using Cylindrical Manifold Embedding
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Learning deformable shape manifolds
Pattern Recognition
Nonlinear shape manifolds as shape priors in level set segmentation and tracking
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a new joint view-identity manifold (JVIM) for multi-view and multi-target shape modeling that is well-suited for automated target tracking and recognition (ATR) in infrared imagery. As a shape generative model, JVIM features a novel manifold structure that imposes a conditional dependency between the two shape-related factors, view and identity, in a unified latent space, which is embedded with one view-independent identity manifold and infinite identity-dependent view manifolds. A modified local linear Gaussian process latent variable model (LL-GPLVM) is proposed for JVIM learning where a stochastic gradient descent method is used to improve the learning efficiency. We also develop a local inference technique to speed up JVIM-based shape interpolation. Due to its probabilistic and continuous nature, JVIM provides effective shape synthesis and supports robust ATR inference for both known and unknown target types under arbitrary views. Experiments on both synthetic data and the SENSIAC infrared ATR database demonstrate the advantages of the proposed method over several existing techniques both qualitatively and quantitatively.