Robust Averaged Projections Onto Convex Sets
Knowing that all previous methods result in perfect sensor prediction when the data is noise-free, we introduce a robust algorithm that enables the user to heavily dampen the impact of noise and outliers on the solution. By controlling the effect of noise we show that the only additional constraint needed is the physically feasible non-negativity. Despite being iterative the method is computationally fast and simple to implement.
To evaluate the new method, we used data from real trichromatic camera systems as well as simulated data. The results support our assertions that controlling the noise results in better sensor estimates.
Document Type: Research Article
Publication date: January 1, 2008
Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.
Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Membership Information
- Terms & Conditions
- Ingenta Connect is not responsible for the content or availability of external websites