K. Madani

Paris-Est Sup, Laboratoire Images, signaux et systèmes intelligents, Université Paris-Est Créteil

Papers

Total Citations

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About

K. Madani’s research lies at the intersection of computer vision, robotics, and intelligent control systems, with a particular focus on developing algorithms that enable machines to perceive and interact with complex environments. His most-cited work, “Image segmentation on spherical coordinate representation of RGB colour space” (2012, 24 citations), introduces a novel segmentation algorithm that leverages a hybrid chromatic distance inspired by the human vision system. This approach dynamically shifts between chromatic and greyscale distance based on pixel luminance, offering a more perceptually accurate method for image analysis. In robotics, Madani has explored obstacle avoidance for biped robots using fuzzy Q-learning (2008, 4 citations), demonstrating how reinforcement learning can enhance autonomous navigation. He has also contributed to neural network optimization, proposing a self-optimizing structure for CMAC neural networks (2010, 2 citations) to address computational challenges in real-time control applications like robot and aircraft control. Additionally, his work on machine learning for heterogeneous multi-robot systems in logistics (2011, 2 citations) highlights his interest in scalable, cooperative robotic systems. Though his citation counts are modest, Madani’s research reflects a thoughtful integration of bio-inspired perception and adaptive learning, offering foundational insights for students and researchers working on vision-based robotics and intelligent control.