| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330319 |
| 005 | | 20241029140740 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438343146 |
| 035 | |
▼a (MiAaPQ)AAI10843245 |
| 035 | |
▼a (MiAaPQ)cornellgrad:10962 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Upchurch, Paul Robert.
▼0 (orcid)0000-0001-9293-7367 |
| 245 | 10 |
▼a Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks. |
| 260 | |
▼a [S.l.] :
▼b Cornell University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 118 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Kavita Bala. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a Fully automatic processing of images is a key challenge for the 21st century. Our processing needs lie beyond just organizing photos by date and location. We need image analysis tools that can reason about photos like a human. For example, we ne |
| 520 | |
▼a The goal of scene understanding is to infer a structured model of reality from a photo. This cannot be done perfectly because there can be many realities which produce the same image. Humans excel at using prior experience to guess the reality w |
| 520 | |
▼a In this thesis we explore the three steps of deep learning through the lens of recognizing materials in a real-world scene and making structured changes to an image: we describe a practical method for efficiently gathering crowdsourced labels |
| 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=T14999911
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |