| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000331349 |
| 005 | | 20241115153605 |
| 008 | | 181129s2017 ||| | | | eng d |
| 020 | |
▼a 9780438165571 |
| 035 | |
▼a (MiAaPQ)AAI10906456 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Cao, Zheng. |
| 245 | 10 |
▼a Information Theoretic Classification of Marine Animal Imagery. |
| 260 | |
▼a [S.l.] :
▼b University of Florida.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 108 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Florida, 2017. |
| 520 | |
▼a To help analyze marine animals behavior, seasonal distribution and abundance, digital imagery can be acquired by Lidar or optical camera. The Unobtrusive Multistatic Serial Lidar Imager (UMSLI) system is designed to collect and classify Lidar im |
| 520 | |
▼a For the purpose of classifying optical images, convolutional neural network (CNN) features are extracted and are tested on two real-world marine animal datasets, yielding better classification results than existing approaches that use hand-desig |
| 520 | |
▼a For both cases of dissimilarity matrices derived from different shape analysis methods (shape context, internal distance shape context, etc.) and features (shape, color, texture, etc.), multi-view learning is critical in integrating more than on |
| 590 | |
▼a School code: 0070. |
| 650 | 4 |
▼a Electrical engineering. |
| 690 | |
▼a 0544 |
| 710 | 20 |
▼a University of Florida.
▼b Electrical and Computer Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0070 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000762
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |