Seyyed Ali Hoseini

Iran University of Science and Technology

Papers

Total Citations

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About

Seyyed Ali Hoseini is a computer vision researcher whose work focuses on advancing visual simultaneous localization and mapping (SLAM) and augmented reality (AR) systems. His primary research areas include feature-based monocular SLAM, camera tracking, and robust map construction in unknown environments. Hoseini’s most cited paper, "A Novel Feature-Based Approach for Indoor Monocular SLAM" (2018), addresses persistent challenges in computer vision and robotics by proposing innovative methods for accurate camera pose estimation and environmental mapping. This work has garnered 9 citations, reflecting its relevance to researchers tackling the difficulties of indoor navigation and AR applications. By enhancing the performance of monocular SLAM systems, Hoseini contributes to the development of more reliable autonomous navigation and immersive augmented reality experiences. His research is particularly valuable for applications in robotics, autonomous vehicles, and mobile AR devices, where precise spatial understanding is critical. Hoseini’s contributions underscore his commitment to solving foundational problems in visual perception and mapping, making his work a reference point for those exploring efficient, feature-based approaches in constrained indoor settings.