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Natural image understanding using algorithm selection and high-level feedback

Natural image understanding using algorithm selection and high-level feedback 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of SPIE SPIE

Natural image understanding using algorithm selection and high-level feedback

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Publisher
SPIE
Copyright
COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
ISSN
0277-786X
eISSN
1996-756X
DOI
10.1117/12.2008593
Publisher site
See Article on Publisher Site

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.

Journal

Proceedings of SPIESPIE

Published: Jan 22, 2013

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