| LDR | | 02033nam u200397 4500 |
| 001 | | 000000419691 |
| 005 | | 20190215163858 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438344464 |
| 035 | |
▼a (MiAaPQ)AAI10928229 |
| 035 | |
▼a (MiAaPQ)cornellgrad:11054 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Wang, Chen. |
| 245 | 10 |
▼a Persistency Algorithms for Efficient Inference in Markov Random Fields. |
| 260 | |
▼a [S.l.]:
▼b Cornell University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 222 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Ramin Zabih. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a Markov Random Fields (MRFs) have achieved great success in a variety of computer vision problems, including image segmentation, stereo estimation, optical flow and image denoising, during the past 20 years. Despite the inference problem being NP |
| 520 | |
▼a In particular, we will explore two different lines of research. The first direction focuses on generalizing the sufficient local condition to check persistency on a set of variables as opposed to a single variable in previous works, and provides |
| 520 | |
▼a This thesis will present a literature study of persistency used for MRF inference, the mathematical formalization of the algorithms and the experimental results for both the first-order and higher-order MRF inference problems. |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a Cornell University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000889
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |