publications

2025

1. 

   Robust camera-independent color chart localization using YOLO

   Luca Cogo, Marco Buzzelli, Simone Bianco, and Raimondo Schettini

   Pattern Recognition Letters, 2025

   Abs DOI Bib PDF Code

   Accurate color information plays a critical role in numerous computer vision tasks, with the Macbeth ColorChecker being a widely used reference target due to its colorimetrically characterized color patches. However, automating the precise extraction of color information in complex scenes remains a challenge. In this paper, we propose a novel method for the automatic detection and accurate extraction of color information from Macbeth ColorCheckers in challenging environments. Our approach involves two distinct phases: (i) a chart localization step using a deep learning model to identify the presence of the ColorChecker, and (ii) a consensus-based pose estimation and color extraction phase that ensures precise localization and description of individual color patches. We rigorously evaluate our method using the widely adopted NUS and ColorChecker datasets. Comparative results against state-of-the-art methods show that our method outperforms the best solution in the state of the art achieving about 5% improvement on the ColorChecker dataset and about 17% on the NUS dataset. Furthermore, the design of our approach enables it to handle the presence of multiple ColorCheckers in complex scenes. Code will be made available after pubblication at: https://github.com/LucaCogo/ColorChartLocalization.

   @article{cogo2025robust,
     title = {Robust camera-independent color chart localization using YOLO},
     journal = {Pattern Recognition Letters},
     volume = {192},
     pages = {51-58},
     year = {2025},
     issn = {0167-8655},
     doi = {https://doi.org/10.1016/j.patrec.2025.03.022},
     url = {https://www.sciencedirect.com/science/article/pii/S0167865525001138},
     author = {Cogo, Luca and Buzzelli, Marco and Bianco, Simone and Schettini, Raimondo},
     keywords = {Object detection, Color target, Pose estimation},
   }

2024

1. 

   RGB illuminant compensation using multi-spectral information

   Mirko Agarla, Simone Bianco, Marco Buzzelli, Luca Cogo, Ilaria Erba, Matteo Kolyszko, Raimondo Schettini, and Simone Zini

   XIX Color Conference, 2024

   Abs PDF

   Multispectral imaging is a technique that captures data across several bands of the light spectrum, in this contribute we report our research related to its application to illuminant estimation an correction in RGB domains. In particular, we present 1. a method that exploits multispectral imaging for illuminant estimation, and then applies illuminant correction in the raw RGB domain to achieve computational color constancy. 2. A method that combines the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. 3. A method that recovers as accurately as possible the spectral information of both the image and the illuminant using Spectral Super Resolution techniques, and exploits a weighted spectral compensation technique that optimizes compensate for possible spectral to perform effective color correction.