Bashar Igried

Papers

Total Citations

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About

Bashar Igried is a researcher whose work sits at the intersection of computer vision and artificial intelligence, with a particular focus on object detection in complex, real-world environments. His key research areas include feature extraction, RGB-D scene understanding, and the development of hybrid models that combine depth and color information to improve detection accuracy. In his most-cited work, "Hybrid features for object detection in RGB-D scenes" (2021), Igried addresses a critical challenge in computer vision: how to effectively integrate depth data from RGB-D sensors with traditional visual features. This contribution is especially relevant for applications in robotics, surveillance, and fine-grained activity recognition, where spatial awareness is essential. While his citation count is still growing—reflecting an early-career trajectory—his work demonstrates a clear commitment to advancing practical, deployable AI systems. Igried’s research stands out for its focus on bridging the gap between theoretical feature engineering and real-world scene complexity, making his contributions valuable for students and engineers working on autonomous systems and intelligent perception.