Paper
12 May 2005 Identification in static luminance and color noise
Piet Bijl, Marcel P. Lucassen, Jolanda Roelofsen
Author Affiliations +
Abstract
If images from multiple sources (e.g. from the different bands of a multi-band sensor) are displayed in color, Signal and Noise may appear as luminance and color differences in the image. As a consequence, the perception of color differences may be important for Target Acquisition performance with fused imagery. Luminance and color can be represented in a 3-D space; in the CIE 1994 color difference model, the three perceptual directions are lightness (L*), chroma (C*) and hue (h*). In this 3-D color space, we performed two perception experiments. In Experiment 1, we measured human observer detection thresholds (JND's) for uniformly distributed static noise (fixed pattern noise) in L*, C* or h* on a uniform background. The results show that the JND for noise in L* is significantly lower than for noise in C* or h*. In Experiment 2, we measured the threshold contrast for identification (orientation discrimination) of a Ushaped test target on a noisy background. With test symbol and background noise in L*, the ratio between signal threshold and noise level is constant. With the symbol in a different direction, we found little dependency on noise level. The results may be used to optimize the use of color to human detection and identification performance with multi-band systems.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Piet Bijl, Marcel P. Lucassen, and Jolanda Roelofsen "Identification in static luminance and color noise", Proc. SPIE 5784, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVI, (12 May 2005); https://doi.org/10.1117/12.602242
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Signal to noise ratio

Target detection

Sensors

Target acquisition

Interference (communication)

Target recognition

3D acquisition

RELATED CONTENT


Back to Top