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Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks. |
| 개인저자 | Upchurch, Paul Robert. |
| 단체저자명 | Cornell University. Computer Science. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 118 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438343146 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Kavita Bala. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
