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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

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Published on: December 3, 2013

Error analysis for image-based rendering with depth information.

Ha Thai Nguyen1, Minh N Do

  • 1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. thai-ha.nguyen@m4x.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 13, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new method to analyze image-based rendering (IBR) quality using depth data. Our approach bounds rendering errors based on camera setup and scene properties, improving IBR algorithm analysis.

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Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

  • Computer Vision
  • Computer Graphics
  • Image Processing

Background:

  • Image-based rendering (IBR) synthesizes novel views from existing images.
  • Accurate rendering quality analysis is crucial for IBR algorithm development.
  • Depth information significantly impacts IBR quality.

Purpose of the Study:

  • To propose a quantitative analysis method for IBR rendering quality using depth information.
  • To establish error bounds for synthesized views in IBR.
  • To investigate the influence of various IBR parameters on rendering accuracy.

Main Methods:

  • Developing a theoretical framework to bound IBR errors.
  • Analyzing the impact of depth and intensity estimate errors.
  • Quantifying the effects of camera configuration, scene properties, occlusions, and discontinuities.
  • Conducting experiments on synthetic and real scenes.

Main Results:

  • Derived error bounds dependent on IBR configurations (depth/intensity errors, scene geometry/texture, camera count/positions/resolution).
  • Identified three key terms contributing to IBR error: camera density, noise level, and depth accuracy.
  • Validated that error bounds accurately characterize rendering errors in experiments.
  • Showcased decay rates of mean absolute errors as O(lambda(-1)) for 2-D and O(lambda(-2)) for 3-D scenes, where lambda is sample density.

Conclusions:

  • The proposed error bounds provide accurate characterization of IBR rendering quality.
  • The analysis offers insights into optimizing camera placement, budget, and bit allocation for IBR.
  • The methodology is broadly applicable to common IBR algorithms and can be applied locally.