4 February 2013 Natural image understanding using algorithm selection and high-level feedback
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Proceedings Volume 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques; 86620D (2013); doi: 10.1117/12.2008593
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
Abstract
Natural Image processing and understanding encompasses hundreds or even thousands of different algorithms. Each algorithm has a certain peak performance for a particular set of input features and configurations of the objects/regions of the input image (environment). To obtain the best possible result of processing, we propose an algorithm selection approach that permits to always use the most appropriate algorithm for the given input image. This is obtained by at first selecting an algorithm based on low level features such as color intensity, histograms, spectral coefficients. The resulting high level image description is then analyzed for logical inconsistencies (contradictions) that are then used to refine the selection of the processing elements. The feedback created from the contradiction information is executed by a Bayesian Network that integrates both the features and a higher level information selection processes. The selection stops when the high level inconsistencies are all resolved or no more different algorithms can be selected.
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Martin Lukac, Michitaka Kameyama, Kosuke Hiura, "Natural image understanding using algorithm selection and high-level feedback", Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620D (4 February 2013); doi: 10.1117/12.2008593; http://dx.doi.org/10.1117/12.2008593
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KEYWORDS
Detection and tracking algorithms

Image processing

Image processing algorithms and systems

Image segmentation

Image understanding

Machine learning

Feature extraction

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