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Improving region based CNN object detector using bayesian optimization
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,...
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AUC Knowledge Fountain
2018
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| _version_ | 1867613409647788032 |
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| access_status_str | Open Access |
| author | Muhammad, Amgad |
| author_browse | Muhammad, Amgad |
| author_facet | Muhammad, Amgad |
| author_sort | Muhammad, Amgad |
| collection | Thesis |
| dc_rights_str_mv | 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. |
| description | 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. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-1429 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:41.195Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-1429 Improving region based CNN object detector using bayesian optimization Muhammad, Amgad 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. 2018-06-01T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/430 https://fount.aucegypt.edu/context/etds/article/1429/viewcontent/Amgad_Muhammad_Thesis.pdf 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. Theses and Dissertations AUC Knowledge Fountain computer vision CNN |
| spellingShingle | computer vision CNN Muhammad, Amgad Improving region based CNN object detector using bayesian optimization |
| title | Improving region based CNN object detector using bayesian optimization |
| title_full | Improving region based CNN object detector using bayesian optimization |
| title_fullStr | Improving region based CNN object detector using bayesian optimization |
| title_full_unstemmed | Improving region based CNN object detector using bayesian optimization |
| title_short | Improving region based CNN object detector using bayesian optimization |
| title_sort | improving region based cnn object detector using bayesian optimization |
| topic | computer vision CNN |
| url | https://fount.aucegypt.edu/etds/430 https://fount.aucegypt.edu/context/etds/article/1429/viewcontent/Amgad_Muhammad_Thesis.pdf |
| work_keys_str_mv | AT muhammadamgad improvingregionbasedcnnobjectdetectorusingbayesianoptimization |