Knowledge based pattern-context-aware stereo analysis and its applications

Charles Z. Liu, Manolya Kavakli

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publication2016 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2016
EditorsAlan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumenstein, Zhiyong Wang
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Electronic)9781509028962
DOIs
Publication statusPublished - 22 Dec 2016
Event2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 - Gold Coast, Australia
Duration: 30 Nov 20162 Dec 2016

Other

Other2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
CountryAustralia
CityGold Coast
Period30/11/162/12/16

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