| 000 | 00000nam c2200205 c 4500 | |
| 001 | 000046026313 | |
| 005 | 20230526121941 | |
| 007 | ta | |
| 008 | 200106s2020 ulkad bmAC 000c eng | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 041 | 0 | ▼a eng ▼b kor |
| 085 | 0 | ▼a 0510 ▼2 KDCP |
| 090 | ▼a 0510 ▼b 6D36 ▼c 1109 | |
| 100 | 1 | ▼a 김남주, ▼g 金楠周 |
| 245 | 1 0 | ▼a Video object removal by learning edge and optical flow / ▼d Nam-joo Kim |
| 260 | ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2020 | |
| 300 | ▼a v, 45장 : ▼b 천연색삽화, 도표 ; ▼c 26 cm | |
| 500 | ▼a 지도교수: 이성환 | |
| 502 | 0 | ▼a 학위논문(석사)-- ▼b 고려대학교 대학원, ▼c 컴퓨터·전파통신공학과, ▼d 2020. 2 |
| 504 | ▼a 참고문헌: 장 40-456 | |
| 530 | ▼a PDF 파일로도 이용가능; ▼c Requires PDF file reader(application/pdf) | |
| 653 | ▼a computer vision | |
| 776 | 0 | ▼t Video Object Removal by Learning Edge and Optical Flow ▼w (DCOLL211009)000000127350 |
| 900 | 1 0 | ▼a Kim, Nam-joo, ▼e 저 |
| 900 | 1 0 | ▼a 이성환, ▼g 李晟瑍, ▼d 1962-, ▼e 지도교수 ▼0 AUTH(211009)151678 |
| 945 | ▼a KLPA |
Electronic Information
| No. | Title | Service |
|---|---|---|
| 1 | Video object removal by learning edge and optical flow (21회 열람) |
View PDF Abstract Table of Contents |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1109 | Accession No. 123063733 | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Science & Engineering Library/Stacks(Thesis)/ | Call Number 0510 6D36 1109 | Accession No. 123063734 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Abstract
Video object removal, when given a video, aims to delete a particular object from the image and to plausibly fill the background in place. Image object removal has been studied tremendously since the rise of deep learning, whereas research in the video domain has not been studied due to challenging points of preserving spatio-temporal coherence and a large amount of computation. In spite of these difficulties, most media in real life are composed of video, so research is necessary. In this work, we propose a network that performs video object removal using edge and optical flow information based on GAN network. The proposed network consists of three sub-networks; learning the edge information to create a background contour that is likely to be at the location of the object to be erased, translating the background pixel at the position of the object through the temporal relationship of the frames in the input video, and inpainting an image using edge information in a reduced empty space in preceding steps. Our method is evaluated on DAVIS datasets qualitatively and quantitatively, achieving the state-of-the-art performance in terms of object removal quality and speed.
Table of Contents
Contents 1. Introduction - 1 - 2. Related Works - 5 - 2.1 Video Inpainting - 6 - 2.2 Edge with Inpainting - 12 - 2.3 Optical Flow - 15 - 3. The Proposed Method - 17 - 3.1 Edge Generator - 20 - 3.2 Flow Guided Frame Warping - 23 - 3.3 Video Completion Network - 25 - 4. Experiments - 28 - 4.1. Implementation Details. - 28 - 4.2. The Quantitative evaluation with the State-of-the-art Methods - 29 - 4.3. The Qualitative evaluation with the State-of-the-art Methods - 31 - 4.4. User study - 38 - 5. Conclusion & Future Works - 39 - References - 40 -
