| LDR | | 01945nam u200409 4500 |
| 001 | | 000000418283 |
| 005 | | 20190215162733 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438139770 |
| 035 | |
▼a (MiAaPQ)AAI10688744 |
| 035 | |
▼a (MiAaPQ)umd:18703 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001.5 |
| 100 | 1 |
▼a Du, Xianzhi. |
| 245 | 10 |
▼a Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 137 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Advisers: Larry Davis |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2017. |
| 520 | |
▼a There are three work on signature matching for document analysis. In the first work, we propose a large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signa |
| 520 | |
▼a There are three work on deep learning for object detection and segmentation. In the first work, we propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for pa |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996768
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |