Illuminant and light direction estimation using Wasserstein distance method
This addresses the problem of robust environmental perception under varying lighting conditions for robotics, though it appears incremental as it builds on optimal transport theory.
The paper tackled illumination estimation in images for robotics by introducing a Wasserstein distance method, which outperformed traditional statistical approaches in detecting dominant light sources and estimating their directions across diverse scenes.
Illumination estimation remains a pivotal challenge in image processing, particularly for robotics, where robust environmental perception is essential under varying lighting conditions. Traditional approaches, such as RGB histograms and GIST descriptors, often fail in complex scenarios due to their sensitivity to illumination changes. This study introduces a novel method utilizing the Wasserstein distance, rooted in optimal transport theory, to estimate illuminant and light direction in images. Experiments on diverse images indoor scenes, black-and-white photographs, and night images demonstrate the method's efficacy in detecting dominant light sources and estimating their directions, outperforming traditional statistical methods in complex lighting environments. The approach shows promise for applications in light source localization, image quality assessment, and object detection enhancement. Future research may explore adaptive thresholding and integrate gradient analysis to enhance accuracy, offering a scalable solution for real-world illumination challenges in robotics and beyond.