A data-driven approach to object classification through fog
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119716 |
| _version_ | 1811088418108604416 |
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| author | Saxena, Alisha |
| author2 | Ramesh Raskar. |
| author_facet | Ramesh Raskar. Saxena, Alisha |
| author_sort | Saxena, Alisha |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T14:01:52Z |
| format | Thesis |
| id | mit-1721.1/119716 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:01:52Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197162019-04-11T09:58:04Z A data-driven approach to object classification through fog Saxena, Alisha Ramesh Raskar. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 51). Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog. by Alisha Saxena. M. Eng. 2018-12-18T19:47:01Z 2018-12-18T19:47:01Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119716 1078637047 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 51 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Saxena, Alisha A data-driven approach to object classification through fog |
| title | A data-driven approach to object classification through fog |
| title_full | A data-driven approach to object classification through fog |
| title_fullStr | A data-driven approach to object classification through fog |
| title_full_unstemmed | A data-driven approach to object classification through fog |
| title_short | A data-driven approach to object classification through fog |
| title_sort | data driven approach to object classification through fog |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119716 |
| work_keys_str_mv | AT saxenaalisha adatadrivenapproachtoobjectclassificationthroughfog AT saxenaalisha datadrivenapproachtoobjectclassificationthroughfog |