Glossary

Contents

  1. What is Spectral RTI and do I need it? (LINK)
    1. Cost/benefit of adding RTI (LINK)
    2. Cost/benefit of adding spectral (LINK)
    3. How does the integration work? (LINK)
  2. Capture hardware (LINK)
    1. Spectral imaging hardware (LINK)
    2. Fixed dome for RTI capture (LINK)
    3. Swinging arc for RTI capture (LINK)
    4. Hand-held flash for RTI capture (LINK)
  3. Capture procedures (LINK)
    1. Light position capture and calibration (LINK)
    2. Hemisphere captures for RTI (LINK)
    3. Preprocessing typically done at capture (LINK)
  4. Processing with the SpectralRTI_Toolkit (LINK)
    1. Software installation (LINK)
    2. Managing input data (LINK)
    3. Running for the first time (LINK)
    4. Select tasks (LINK)
    5. Adjust brightness (LINK)
    6. Static raking option (LINK)
    7. Options for Extended Spectrum (LINK)
    8. Identify region of interest (for Extended Spectrum and Pseudocolor) (LINK)
    9. Options for PCA Pseudocolor (LINK)
    10. Options for custom processes (LINK)
    11. Light position data (LINK)
    12. Additional prompts (LINK)
    13. Tips for frequent use (LINK)
  5. Make the images accessible to users (LINK)
    1. RTI files (LINK)
    2. WebRTI (LINK)
    3. Tiled and layered Jpeg2000 files for IIIF (LINK)
  6. Help make Spectral RTI better (LINK)
  7. Glossary
Extended Spectrum
A processing technique for multispectral captures that squeezes the contrasts detectable in the full range of the spectral capture into the range of the spectrum visible to humans. Thus a pixel that reflects UV would appear blue and IR would appear red.
It works by using Principal Component Analysis to find the greatest contrast (first principal component) in the shortest wavelength captures and rendering that as blue, and similarly for the middle (green) and longest (red) wavelengths.
PCA Pseudocolor
A processing technique for multispectral captures that finds the greatest contrasts detectable in the full range of spectral capture and renders those contrasts in visible to humans with no semblance of natural appearance.
It works by using Principal Component Analysis to find up to three contrasts (principal components) and rendering those as the Red, Green, and Blue channels of an RGB image (or similarly two components as the Cb and Cr channels of a YCbCr image).