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 - The International Society for Optical Engineering |
| Volume | 8662 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | Intelligent Robots and Computer Vision XXX: Algorithms and Techniques - Burlingame, CA, United States Duration: Feb 4 2013 → Feb 6 2013 |
Other
| Other | Intelligent Robots and Computer Vision XXX: Algorithms and Techniques |
|---|---|
| Country | United States |
| City | Burlingame, CA |
| Period | 2/4/13 → 2/6/13 |
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Keywords
- Algorithm Selection
- Bayesian Networks
- Contour extraction
- Image Processing
ASJC Scopus subject areas
- Applied Mathematics
- Computer Science Applications
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
Cite this
Natural image understanding using algorithm selection and high-level feedback. / Lukac, Martin; Kameyama, Michitaka; Hiura, Kosuke.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 8662 2013. 86620D.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Natural image understanding using algorithm selection and high-level feedback
AU - Lukac, Martin
AU - Kameyama, Michitaka
AU - Hiura, Kosuke
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Algorithm Selection
KW - Bayesian Networks
KW - Contour extraction
KW - Image Processing
UR - http://www.scopus.com/inward/record.url?scp=84875891053&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875891053&partnerID=8YFLogxK
U2 - 10.1117/12.2008593
DO - 10.1117/12.2008593
M3 - Conference contribution
AN - SCOPUS:84875891053
SN - 9780819494351
VL - 8662
BT - Proceedings of SPIE - The International Society for Optical Engineering
ER -