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Visual analysis and synthesis with physically grounded constraints
Huang, Jia-Bin
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https://hdl.handle.net/2142/92750
Description
- Title
- Visual analysis and synthesis with physically grounded constraints
- Author(s)
- Huang, Jia-Bin
- Issue Date
- 2016-07-06
- Director of Research (if dissertation) or Advisor (if thesis)
- Ahuja, Narendra
- Doctoral Committee Chair(s)
- Ahuja, Narendra
- Committee Member(s)
- Huang, Thomas S.
- Do, Minh N.
- Hasegawa-Johnson, Mark
- Hoiem, Derek
- Department of Study
- Electrical & Computer Eng
- Discipline
- Electrical & Computer Engr
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- Computer vision
- Visual synthesis
- patch-based optimization
- image completion
- image super-resolution
- video completion
- visual tracking
- Abstract
- "The past decade has witnessed remarkable progress in image-based, data-driven vision and graphics. However, existing approaches often treat the images as pure 2D signals and not as a 2D projection of the physical 3D world. As a result, a lot of training examples are required to cover sufficiently diverse appearances and inevitably suffer from limited generalization capability. In this thesis, I propose ""inference-by-composition"" approaches to overcome these limitations by modeling and interpreting visual signals in terms of physical surface, object, and scene. I show how we can incorporate physically grounded constraints such as scene-specific geometry in a non-parametric optimization framework for (1) revealing the missing parts of an image due to removal of a foreground or background element, (2) recovering high spatial frequency details that are not resolvable in low-resolution observations. I then extend the framework from 2D images to handle spatio-temporal visual data (videos). I demonstrate that we can convincingly fill spatio-temporal holes in a temporally coherent fashion by jointly reconstructing the appearance and motion. Compared to existing approaches, our technique can synthesize physically plausible contents even in challenging videos. For visual analysis, I apply stereo camera constraints for discovering multiple approximately linear structures in extremely noisy videos with an ecological application to bird migration monitoring at night. The resulting algorithms are simple and intuitive while achieving state-of-the-art performance without the need of training on an exhaustive set of visual examples."
- Graduation Semester
- 2016-08
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/92750
- Copyright and License Information
- Copyright 2016 Jia-Bin Huang
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Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer EngineeringGraduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
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