A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion
International Journal of Computer Vision
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Robust 3D Pose Estimation and Efficient 2D Region-Based Segmentation from a 3D Shape Prior
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
PWP3D: Real-Time Segmentation and Tracking of 3D Objects
International Journal of Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a novel framework for joint 2D segmentation and 3D pose and 3D shape recovery, for images coming from a single monocular source. In the past, integration of all three has proven difficult, largely because of the high degree of ambiguity in the 2D - 3D mapping. Our solution is to learn nonlinear and probabilistic low dimensional latent spaces, using the Gaussian Process Latent Variable Models dimensionality reduction technique. These act as class or activity constraints to a simultaneous and variational segmentation --- recovery --- reconstruction process. We define an image and level set based energy function, which we minimise with respect to 3D pose and shape, 2D segmentation resulting automatically as the projection of the recovered shape under the recovered pose. We represent 3D shapes as zero levels of 3D level set embedding functions, which we project down directly to probabilistic 2D occupancy maps, without the requirement of an intermediary explicit contour stage. Finally, we detail a fast, open-source, GPU-based implementation of our algorithm, which we use to produce results on both real and artificial video sequences.