A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Probablistic Affine Invariants for Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Viewpoint-Invariant Learning and Detection of Human Heads
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Monocular Perception of Biological Motion Detection and Labeling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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When we are born we do not know about sailing boats, frogs, cell-phones and wheelbarrows. By the time we reach school age we can easily recognize these categories of objects and many more using our visual system; by some estimates, we learn around 10 new categories per day with minimal supervision during the first few years of our lives. How can this happen? I will outline a computational approach to the problem of representing the visual properties of object categories, and of learning such models without supervision from cluttered images. Both static images of objects and dynamic displays such as the ones generated by human activity are handled by the theory. Its properties will be exemplified with experiments on a variety of categories.