Unsupervised learning of object features from video sequences
We develop an efficient algorithm for unsupervised learning of object models as constellations of features, from low resolution video sequences. The input images typically contain single or multiple objects that change in pose, scale and degree of occlusion. Also, the objects can move significantly...
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Published in | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 1142 - 1149 vol. 1 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
2005
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Subjects | |
Online Access | Get full text |
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