  In this paper, we propose a complete framework to process images captured
under uncontrolled lighting and especially under low lighting. By taking
advantage of the Logarithmic Image Processing (LIP) context, we study two novel
functional metrics: i) the LIP-multiplicative Asplund metric which is robust to
object absorption variations and ii) the LIP-additive Asplund metric which is
robust to variations of source intensity or camera exposure-time. We introduce
robust to noise versions of these metrics. We demonstrate that the maps of
their corresponding distances between an image and a reference template are
linked to Mathematical Morphology. This facilitates their implementation. We
assess them in various situations with different lightings and movement.
Results show that those maps of distances are robust to lighting variations.
Importantly, they are efficient to detect patterns in low-contrast images with
a template acquired under a different lighting.
