Natural image understanding using algorithm selection and high-level feedback

Martin Lukac; Michitaka Kameyama; Kosuke Hiura

doi:10.1117/12.2008593

Natural image understanding using algorithm selection and high-level feedback

Martin Lukac, Michitaka Kameyama, Kosuke Hiura

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX
Subtitle of host publication	Algorithms and Techniques
DOIs	https://doi.org/10.1117/12.2008593
Publication status	Published - 2013
Event	Intelligent Robots and Computer Vision XXX: Algorithms and Techniques - Burlingame, CA, United States Duration: Feb 4 2013 → Feb 6 2013

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8662
ISSN (Print)	0277-786X

Other

Other	Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Country	United States
City	Burlingame, CA
Period	2/4/13 → 2/6/13

Keywords

Algorithm Selection
Bayesian Networks
Contour extraction
Image Processing

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2008593

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Cite this

Lukac, M., Kameyama, M., & Hiura, K. (2013). Natural image understanding using algorithm selection and high-level feedback. In Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX: Algorithms and Techniques [86620D] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8662). https://doi.org/10.1117/12.2008593

Natural image understanding using algorithm selection and high-level feedback. / Lukac, Martin; Kameyama, Michitaka; Hiura, Kosuke.

Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX: Algorithms and Techniques. 2013. 86620D (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8662).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lukac, M, Kameyama, M & Hiura, K 2013, Natural image understanding using algorithm selection and high-level feedback. in Proceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXX: Algorithms and Techniques., 86620D, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, Burlingame, CA, United States, 2/4/13. https://doi.org/10.1117/12.2008593

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