F.J. Madrid-Cuevas

Papers

Total Citations

2,502

H-Index

About

F.J. Madrid-Cuevas is a leading researcher in computer vision and pattern recognition, with a particular focus on fiducial marker systems and their robust detection under challenging conditions. His most influential contribution is the development of ArUco markers, a groundbreaking fiducial marker library that has become a de facto standard in augmented reality, robotics, and camera calibration. His seminal 2014 paper, "Automatic generation and detection of highly reliable fiducial markers under occlusion," has garnered over 2,500 citations, underscoring its transformative impact on the field. The work introduced a novel method for generating and detecting markers that maintain high reliability even when partially occluded—a critical advancement for real-world applications. Beyond this, Madrid-Cuevas has made significant strides in image segmentation, 3D reconstruction, and agricultural computer vision, contributing to over 100 peer-reviewed publications. His research is characterized by a practical, application-driven approach that bridges theoretical algorithms with deployable systems. For students and researchers, his work exemplifies how elegant mathematical solutions to occlusion and distortion problems can enable robust, real-time performance in uncontrolled environments, making him a pivotal figure in modern computer vision.