GTM: the generative topographic mapping
Neural Computation
International Journal of Computer Vision
A Coarse-to-Fine Deformable Contour Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Computer-assisted endocardial border identification from a sequence of two-dimensional echocardiographic images
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Nonlinear manifold learning for dynamic shape and dynamic appearance
Computer Vision and Image Understanding
An Algorithm for Finding Intrinsic Dimensionality of Data
IEEE Transactions on Computers
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Robust Shape Tracking With Multiple Models in Ultrasound Images
IEEE Transactions on Image Processing
Online learning neural tracker
Neurocomputing
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This paper presents a novel manifold learning approach for high dimensional data, with emphasis on the problem of motion tracking in video sequences. In this problem, the samples are time-ordered, providing additional information that most current methods do not take advantage of. Additionally, most methods assume that the manifold topology admits a single chart, which is overly restrictive. Instead, the algorithm can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are combined in a probabilistic fashion using Gaussian process regression. Thus, the algorithm is termed herein as Gaussian Process Multiple Local Models (GP-MLM). Additionally, the paper describes a multiple filter architecture where standard filtering techniques, e.g. particle and Kalman filtering, are combined with the output of GP-MLM in a principled way. The performance of this approach is illustrated with experimental results using real video sequences. A comparison with GP-LVM[29] is also provided. Our algorithm achieves competitive state-of-the-art results on a public database concerning the left ventricle (LV) ultrasound (US) and lips images.