ICCV'01, Vancouver, Canada, July, 2001
4-Sensor Camera Calibration for Image Representation Invariant
to Shading, Shadows, Lighting, and Specularities
|Graham D. Finlayson
||Mark S. Drew
|School of Information Systems
||School of Computing Science
|The University of East Anglia
||Simon Fraser University
||Vancouver, British Columbia
|England NR4 7TJ
||Canada V5A 1S6
Paper (version with full-size figures)
Most lighting can be accurately modeled using a simplified Planckian function.
If we form logarithms of color ratios of camera sensor values, then in
a Lambertian plus specular two-lobe model of reflection the temperature-dependent
term is separate and is seen as a straight line: i.e., changing lighting
amounts to changing each pixel value in a straight line, for a given camera.
Here we use a 4-sensor camera. In this case, forming color ratios reduces
the dimensionality to 3. Applying logarithms and projecting onto the plane
in the 3D color space orthogonal to the light-change direction results
in an image representation that is invariant to illumination change. For
a given camera, the position of the specular point in the 2D plane
is always the same, independent of the lighting. Thus a camera calibration
produces illumination invariance at a single pixel. In the plane, matte
surfaces reduce to points and specularities are almost straight lines.
Extending each pixel value back to the matte position, postulated to be
the maximum radius from the fixed specular point, at any angle in the 2D
plane, removes specularity. Thus images are independent of shading (by
forming ratios), independent of shadows (by making them independent of
illumination temperature) and independent of specularities. The method
is examined by forming 4D images from hyperspectral images, using real
camera sensors, with encouraging results.