A Computational Learning Theory of Active Object Recognition Under Uncertainty

We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing...

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Bibliographic Details
Published in:	International journal of computer vision Vol. 101; no. 1; pp. 95 - 142
Main Authors:	Andreopoulos, Alexander, Tsotsos, John K.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.01.2013 Springer Springer Nature B.V
Subjects:	Algorithmics. Computability. Computer arithmetics Analysis Applied sciences Artificial Intelligence Complexity Computation Computer Imaging Computer Science Computer science; control theory; systems Computer vision Costs Data processing. List processing. Character string processing Decision theory Exact sciences and technology Image Processing and Computer Vision Learning Localization Machine learning Memory organisation. Data processing Noise Object recognition Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Representations Searching Sensors Software Studies Theoretical computing Uncertainty Vision Vision systems Computational complexity of vision Active vision Visual search Object recognition Attention Occlusion Complexity class Computer vision Automatic classification Constraint satisfaction Pattern recognition Computational complexity Modeling Noise level Selective attention Upper bound Uncertain system Object detection Problem solving Probability learning Learning algorithm Artificial intelligence Occultation Object location
ISSN:	0920-5691, 1573-1405
Online Access:	Get full text
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