Statistical background modelling for tracking with a virtual camera
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
Learning Layered Motion Segmentations of Video
International Journal of Computer Vision
Approaches and Challenges for Cognitive Vision Systems
Creating Brain-Like Intelligence
Object localisation using the Generative Template of Features
Computer Vision and Image Understanding
Structure inference for Bayesian multisensory perception and tracking
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Simultaneous non-gaussian data clustering, feature selection and outliers rejection
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
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
Unsupervised learning of multiple aspects of moving objects from video
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Expert Systems with Applications: An International Journal
Learning a generative model of images by factoring appearance and shape
Neural Computation
The Shape Boltzmann Machine: A Strong Model of Object Shape
International Journal of Computer Vision
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We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.