Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications

Charles Z. Liu; Manolya Kavakli

doi:10.1109/DICTA.2016.7797058

Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

In this paper, we study a pattern-context-aware scheme for stereo pattern analysis. Depth and texture are chosen as two primary factors for the pattern-context- aware computing. We organize these patterns as a context to analyze.A knowledge-based inference system is built with human experience to model the correlation of the context and processing. The process for the pattern analysis could recognize potentially interested patterns by processing the optimal belief context. The strategy enables the system aware of the uncertainty in the pattern. An enhanced learning approach is introduced to allow the system to process the ambiguous pattern and to refine the confidence. An example is given to show the feasibility of the proposed scheme. It can be seen that the potential patterns can be differentiated and rebuilt as an extracted stereo model with the context-awareness. The discussion on potential applications for the intelligent driving systems is presented.

Original language	English
Title of host publication	2016 International Conference on Digital Image Computing
Subtitle of host publication	Techniques and Applications, DICTA 2016
Editors	Alan Wee-Chung Liew, Jun Zhou, Yongsheng Gao, Zhiyong Wang, Clinton Fookes, Brian Lovell, Michael Blumenstein
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	9781509028962
DOIs	https://doi.org/10.1109/DICTA.2016.7797058
Publication status	Published - 22 Dec 2016
Event	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 - Gold Coast, Australia Duration: 30 Nov 2016 → 2 Dec 2016

Publication series

Name	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016

Conference

Conference	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
Country/Territory	Australia
City	Gold Coast
Period	30/11/16 → 2/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/DICTA.2016.7797058

Cite this

Liu, C. Z., & Kavakli, M. (2016). Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications. In A. W.-C. Liew, J. Zhou, Y. Gao, Z. Wang, C. Fookes, B. Lovell, & M. Blumenstein (Eds.), 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 Article 7797058 (2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016). IEEE. https://doi.org/10.1109/DICTA.2016.7797058

Liu, Charles Z. ; Kavakli, Manolya. / Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications. 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. editor / Alan Wee-Chung Liew ; Jun Zhou ; Yongsheng Gao ; Zhiyong Wang ; Clinton Fookes ; Brian Lovell ; Michael Blumenstein. IEEE, 2016. (2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016).

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title = "Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications",

abstract = "In this paper, we study a pattern-context-aware scheme for stereo pattern analysis. Depth and texture are chosen as two primary factors for the pattern-context- aware computing. We organize these patterns as a context to analyze.A knowledge-based inference system is built with human experience to model the correlation of the context and processing. The process for the pattern analysis could recognize potentially interested patterns by processing the optimal belief context. The strategy enables the system aware of the uncertainty in the pattern. An enhanced learning approach is introduced to allow the system to process the ambiguous pattern and to refine the confidence. An example is given to show the feasibility of the proposed scheme. It can be seen that the potential patterns can be differentiated and rebuilt as an extracted stereo model with the context-awareness. The discussion on potential applications for the intelligent driving systems is presented.",

author = "Liu, {Charles Z.} and Manolya Kavakli",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 ; Conference date: 30-11-2016 Through 02-12-2016",

year = "2016",

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doi = "10.1109/DICTA.2016.7797058",

language = "English",

series = "2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016",

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Liu, CZ & Kavakli, M 2016, Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications. in AW-C Liew, J Zhou, Y Gao, Z Wang, C Fookes, B Lovell & M Blumenstein (eds), 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016., 7797058, 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, IEEE, 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, Gold Coast, Australia, 30/11/16. https://doi.org/10.1109/DICTA.2016.7797058

Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications. / Liu, Charles Z.; Kavakli, Manolya.
2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. ed. / Alan Wee-Chung Liew; Jun Zhou; Yongsheng Gao; Zhiyong Wang; Clinton Fookes; Brian Lovell; Michael Blumenstein. IEEE, 2016. 7797058 (2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications

AU - Liu, Charles Z.

AU - Kavakli, Manolya

PY - 2016/12/22

Y1 - 2016/12/22

N2 - In this paper, we study a pattern-context-aware scheme for stereo pattern analysis. Depth and texture are chosen as two primary factors for the pattern-context- aware computing. We organize these patterns as a context to analyze.A knowledge-based inference system is built with human experience to model the correlation of the context and processing. The process for the pattern analysis could recognize potentially interested patterns by processing the optimal belief context. The strategy enables the system aware of the uncertainty in the pattern. An enhanced learning approach is introduced to allow the system to process the ambiguous pattern and to refine the confidence. An example is given to show the feasibility of the proposed scheme. It can be seen that the potential patterns can be differentiated and rebuilt as an extracted stereo model with the context-awareness. The discussion on potential applications for the intelligent driving systems is presented.

AB - In this paper, we study a pattern-context-aware scheme for stereo pattern analysis. Depth and texture are chosen as two primary factors for the pattern-context- aware computing. We organize these patterns as a context to analyze.A knowledge-based inference system is built with human experience to model the correlation of the context and processing. The process for the pattern analysis could recognize potentially interested patterns by processing the optimal belief context. The strategy enables the system aware of the uncertainty in the pattern. An enhanced learning approach is introduced to allow the system to process the ambiguous pattern and to refine the confidence. An example is given to show the feasibility of the proposed scheme. It can be seen that the potential patterns can be differentiated and rebuilt as an extracted stereo model with the context-awareness. The discussion on potential applications for the intelligent driving systems is presented.

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BT - 2016 International Conference on Digital Image Computing

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Liu CZ, Kavakli M. Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications. In Liew AWC, Zhou J, Gao Y, Wang Z, Fookes C, Lovell B, Blumenstein M, editors, 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. IEEE. 2016. 7797058. (2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016). doi: 10.1109/DICTA.2016.7797058

Knowledge Based Pattern-Context-Aware Stereo Analysis and Its Applications

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