Functional Asplund metrics for pattern matching, robust to variable lighting conditions

TL;DR

This paper introduces two novel Logarithmic Image Processing-based Asplund metrics that are robust to lighting variations and noise, enabling effective pattern matching in uncontrolled and low-light conditions.

Contribution

The paper presents new LIP-multiplicative and additive Asplund metrics, along with noise-robust versions, linking them to Mathematical Morphology for efficient pattern matching under variable lighting.

Findings

Metrics are robust to lighting variations and low contrast.

Distance maps effectively detect patterns across different lighting conditions.

Methods are computationally efficient for real-world applications.

Abstract

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.