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Article
Automatic Objects Removal for Scene Completion
IEEE INFOCOM Workshop on Security and Privacy in Big Data
  • Jianjun Yang, University of North Georgia
  • Yin Wang, Lawrence Technological University
  • Honggang Wang, University of Massachusetts at Dartmouth
  • Kun Hua, Lawrence Technological University
  • Wei Wang, South Dakota State University
  • Ju Shen, University of Dayton
Document Type
Conference Paper
Publication Date
4-1-2014
Abstract
With the explosive growth of Web-based cameras and mobile devices, billions of photographs are uploaded to the Internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.
Inclusive pages
553-558
Document Version
Published Version
Comments

Permission documentation is on file.

Publisher
IEEE
Place of Publication
Toronto, Canada
Peer Reviewed
Yes
Citation Information
Jianjun Yang, Yin Wang, Honggang Wang, Kun Hua, et al.. "Automatic Objects Removal for Scene Completion" IEEE INFOCOM Workshop on Security and Privacy in Big Data (2014)
Available at: http://works.bepress.com/ju_shen/21/