Advances in neural information processing systems 2
Connectionist learning of belief networks
Artificial Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistics and Computing
Training products of experts by minimizing contrastive divergence
Neural Computation
UNSUPERVISED LEARNING OF DISTRIBUTIONS ON BINARY VECTORS USING TWO LAYER NETWORKS
UNSUPERVISED LEARNING OF DISTRIBUTIONS ON BINARY VECTORS USING TWO LAYER NETWORKS
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Hybrid Graphical Model for Robust Feature Extraction from Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
On the quantitative analysis of deep belief networks
Proceedings of the 25th international conference on Machine learning
Training restricted Boltzmann machines using approximations to the likelihood gradient
Proceedings of the 25th international conference on Machine learning
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale deep unsupervised learning using graphics processors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Weakly supervised learning of foreground-background segmentation using masked RBMs
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Shape-based pedestrian parsing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
On deep generative models with applications to recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Inpainting of Binary Images Using the Cahn–Hilliard Equation
IEEE Transactions on Image Processing
Learning a generative model of images by factoring appearance and shape
Neural Computation
Deep Learning Shape Priors for Object Segmentation
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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A good model of object shape is essential in applications such as segmentation, detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shapes can help where object boundaries are noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to parts of the objects. In general, the stronger the model of shape, the more performance is improved. In this paper, we use a type of deep Boltzmann machine (Salakhutdinov and Hinton, International Conference on Artificial Intelligence and Statistics, 2009) that we call a Shape Boltzmann Machine (SBM) for the task of modeling foreground/background (binary) and parts-based (categorical) shape images. We show that the SBM characterizes a strong model of shape, in that samples from the model look realistic and it can generalize to generate samples that differ from training examples. We find that the SBM learns distributions that are qualitatively and quantitatively better than existing models for this task.