Georgios Kordelas

Information Technologies Institute, Centre for Research and Technology Hellas

Papers

Total Citations

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About

Georgios Kordelas is a researcher whose work lies at the intersection of computer vision, image processing, and 3D object recognition. His primary contributions focus on enhancing the robustness and reliability of feature matching algorithms, particularly through innovative improvements to the Scale-Invariant Feature Transform (SIFT). In his highly cited 2009 paper (16 citations), Kordelas introduced a novel method that employs Kendall’s rank correlation measure to strengthen SIFT-based feature matching, making it more resilient to noise and geometric distortions—a critical advancement for applications in machine vision, robot navigation, and object recognition. Building on this foundation, his 2010 work (13 citations) tackled the challenge of viewpoint independent object recognition in cluttered scenes by cleverly integrating ray-triangle intersection algorithms with SIFT, enabling systems to identify objects from arbitrary perspectives even in complex, real-world environments. These contributions have not only advanced the theoretical understanding of feature matching but also provided practical tools for robust visual recognition systems. Kordelas’s research continues to influence fields ranging from autonomous robotics to augmented reality, where reliable object detection under varying conditions is paramount.