Depth map generation of single-view images based on view-distance classification = 단일시점 영상의 장면 분류를 통한 깊이지도 생성

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In this thesis, we propose a method of depth map generation based on view-distance classification. An input image is transformed by using Fourier Transform, and the image is categorized into close-up view, long view with vanishing point, and long view without vanishing point using 2-step Support Vector Machines (SVM) whose feature vectors are the spectra of the input image. In the close-up view case, the depth map is generated using an iterative reversible graph cut method based on saliency maps. In case of long view with vanishing point, the depth map is generated by a vanishing point detection technique. Finally, in the long view without vanishing point case, we use a sky detection algorithm and a gradient plane for depth map generation. We estimate the accuracy of the proposed image classification algorithm through experiments. Subjective evaluation is also conducted to assess our proposed system about depth map generation. Experimental results indicate that our system is competitive.
Advisors
Kim, Chang-Ickresearcher김창익researcher
Description
한국과학기술원 : 정보통신공학과,
Publisher
한국과학기술원
Issue Date
2010
Identifier
419072/325007  / 020084199
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 정보통신공학과, 2010.2, [ vi, 48 p. ]

Keywords

Depth map generation; Image classification; 영상 분류; 깊이지도 생성

URI
http://hdl.handle.net/10203/40096
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=419072&flag=dissertation
Appears in Collection
ICE-Theses_Master(석사논문)
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