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Fast and precise binary image descriptor for autonomous vehicle visual localization / Ahmed Zakaria Abdelkhalek Bibars ; Supervised Magdi Fikri Ragaey , Mohsen Mohamed Mahroos

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Zakaria Abdelkhalek Bibars , 2019Description: 77 P. : charts , photographs ; 30cmOther title:
  • واصف صور ثنائى سريع ودقيق لتحديد موقع المركبات بصريا [Added title page title]
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Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Electronics and Communication Summary: Autonomous vehicle self-localization by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during day hours and appearance changes between year seasons are the major difficulties of this problem. This thesis presents: 1) a new binary image descriptor addressed as 3Extended Local Difference Binary3 (ELDB), which is an extension to the state-of the-art Local Difference Binary (LDB) image descriptor, and 2) a new algorithm for vehicle visual localization under extreme environmental changes that uses Multi-Hypothesis Markov Localization (MHML) as a data fusion algorithm, and uses ELDB for image matching. Experimental results presented in the thesis show that ELDB has better image matching accuracy and computational efficiency than LDB, and that the proposed vehicle visual localization algorithm is faster and more accurate than other state-of-the-art algorithms
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.Ph.D.2019.Ah.F (Browse shelf(Opens below)) Not for loan 01010110079226000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.Ph.D.2019.Ah.F (Browse shelf(Opens below)) 79226.CD Not for loan 01020110079226000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Electronics and Communication

Autonomous vehicle self-localization by scene matching under extreme environmental changes has been among the most challenging problems in robotics and computer vision in the last few years. Large dynamic illumination changes during day hours and appearance changes between year seasons are the major difficulties of this problem. This thesis presents: 1) a new binary image descriptor addressed as 3Extended Local Difference Binary3 (ELDB), which is an extension to the state-of the-art Local Difference Binary (LDB) image descriptor, and 2) a new algorithm for vehicle visual localization under extreme environmental changes that uses Multi-Hypothesis Markov Localization (MHML) as a data fusion algorithm, and uses ELDB for image matching. Experimental results presented in the thesis show that ELDB has better image matching accuracy and computational efficiency than LDB, and that the proposed vehicle visual localization algorithm is faster and more accurate than other state-of-the-art algorithms

Issued also as CD

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