Abstract

Using Deep Neural Networks for object detection tasks has had groundbreaking results on several object detection benchmarks. Although the trained models have high capacity and strong discrimination power, yet inaccurate localization is a major source of error for those detection systems. In my work, I'm developing a sequential searching algorithm based on Bayesian Optimization to propose better candidate bounding boxes for the objects of interest. The work is focusing on formulating effective region proposal as an optimization problem and using Bayesian Optimization algorithm as a black-box optimizer to sequentially solve this problem. The proposed algorithm demonstrated the state-of-the-art performance on PASCAL VOC 2007 benchmark under the standard localization requirements.

Department

Computer Science & Engineering Department

Degree Name

MS in Computer Science

Graduation Date

6-1-2018

Submission Date

February 2018

First Advisor

Moustafa, Mohamed

Committee Member 1

Goneid, Amr

Committee Member 2

Abbas, Hazem

Extent

36 p.

Document Type

Master's Thesis

Rights

The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy.

Institutional Review Board (IRB) Approval

Not necessary for this item

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