Functional Asplund metrics for pattern matching, robust to variable lighting conditions
This addresses the problem of reliable pattern detection in variable lighting for computer vision applications, but it is incremental as it builds on existing LIP and Asplund metric frameworks.
The paper tackles pattern matching in images under uncontrolled lighting, particularly low light, by proposing two functional Asplund metrics based on Logarithmic Image Processing that are robust to absorption and intensity variations, with results showing effectiveness in detecting patterns in low-contrast images.
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.