A Camera-Based Energy Relaxation Framework to Minimize Color Artifacts in a Projected Display

Abstract

We introduce a technique to automatically correct color inconsistencies in a display composed of one or more digital light projectors (DLP). The method is agnostic to the source of error and can detect and address color problems from a number of sources. Examples include inter- and intra-projector color differences, display surface markings, and environmental lighting differences on the display. In contrast to methods that discover and map all colors into the greatest common color space, we minimize local color discontinuities to create color seamlessness while remaining tolerant to significant color error. The technique makes use of a commodity camera and high-dynamic range sensing to measure color gamuts at many different spatial locations. A differentiable energy function is defined that combines both a smoothness and data term. This energy function is globally minimized through the successive application of projective warps defined using gradient descent. At convergence the warps can be applied at runtime to minimize color defects in the display. The framework is demonstrated on displays that suffer from several sources of color

Cite

Text

Sanders and Jaynes. "A Camera-Based Energy Relaxation Framework to Minimize Color Artifacts in a Projected Display." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.8

Markdown

[Sanders and Jaynes. "A Camera-Based Energy Relaxation Framework to Minimize Color Artifacts in a Projected Display." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/sanders2006cvprw-camerabased/) doi:10.1109/CVPRW.2006.8

BibTeX

@inproceedings{sanders2006cvprw-camerabased,
  title     = {{A Camera-Based Energy Relaxation Framework to Minimize Color Artifacts in a Projected Display}},
  author    = {Sanders, Nathaniel and Jaynes, Christopher O.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {7},
  doi       = {10.1109/CVPRW.2006.8},
  url       = {https://mlanthology.org/cvprw/2006/sanders2006cvprw-camerabased/}
}