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Persistency Algorithms for Efficient Inference in Markov Random Fields
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Persistency Algorithms for Efficient Inference in Markov Random Fields. |
| 개인저자 | Wang, Chen. |
| 단체저자명 | Cornell University. Computer Science. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 222 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438344464 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Ramin Zabih. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
