1 / 9

Multi-Class Blue Noise Sampling

This research delves into the efficacy of Multi-Class Blue Noise Sampling methods, particularly as utilized by Microsoft Research. By comparing random and uniform sampling strategies, it examines sample spacing parameters for optimal sensor layout configurations. The study highlights the application of algorithms that are user-friendly while maintaining effectiveness, focusing on RGB sensor distributions and color stippling techniques. The results reveal promising insights for enhancing object placement strategies in various applications, presenting a balanced approach between complexity and usability.

lenore
Télécharger la présentation

Multi-Class Blue Noise Sampling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multi-Class Blue Noise Sampling Li-Yi Wei Microsoft Research

  2. smart↑ → research↑ • Q: not so smart?

  3. Blue noise • random & uniform • sample spacing r

  4. 0.02 0.01 0.01 0.02 r = r = 0.02 single class multi class

  5. Multi-class blue noise class 0 class 1 total set

  6. Object placement

  7. Sensor layout RGB sensors cone/rod cells

  8. Color stippling RGBCMYB dots

  9. A: smart↓ → popular↑ • simpler algorithms • less intimidating too smart # sig10 paper 6 5 5 4

More Related