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.


Computer Science & Engineering Department

Degree Name

MS in Computer Science

Graduation Date


Submission Date

February 2018

First Advisor

Moustafa, Mohamed

Committee Member 1

Goneid, Amr

Committee Member 2

Abbas, Hazem


36 p.

Document Type

Master's Thesis


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Institutional Review Board (IRB) Approval

Not necessary for this item