Taha Hamedani

Papers

Total Citations

H-Index

About

Taha Hamedani’s research lies at the intersection of 3D vision, image processing, and geometric data compression, with a focus on enhancing the quality and efficiency of depth imagery. His work addresses critical challenges in robotic mapping, object recognition, and emerging fields like free-viewpoint and 3D television. In his most-cited paper, “Edge preserving range image smoothing using hybrid locally kernel-based weighted least square” (2022, 9 citations), Hamedani introduced a novel method that preserves sharp edges while effectively smoothing noisy range data—a key requirement for accurate 3D scene interpretation. Earlier, in “Depth image compression using geometrical wavelets” (2014, 3 citations), he tackled the redundancy inherent in high-resolution, high-frame-rate depth sequences, proposing a compression technique that leverages geometric wavelets to reduce data without sacrificing structural fidelity. Though early in his citation trajectory, Hamedani’s contributions demonstrate a clear commitment to practical, real-world improvements in 3D sensing pipelines. His work is particularly relevant for researchers developing efficient, edge-aware algorithms for autonomous systems and immersive media.