Riti Sharma

Rochester Institute of Technology, Pennsylvania State University

Papers

Total Citations

H-Index

About

Riti Sharma’s research lies at the intersection of computer vision, augmented reality, and robotics, with a focus on making object detection and human-machine interaction more reliable and efficient. Her most influential work introduces a lean histogram of oriented gradients (HOG) feature set for effective eye detection, demonstrating how dimensionality reduction can boost the speed and accuracy of hand-crafted features paired with SVM classifiers—a contribution that has garnered 18 citations and remains relevant for real-time vision systems. In her earlier pioneering work on augmented reality (12 citations), Sharma developed a systematic framework for guiding and evaluating mechanical assembly sequences, drawing on robot assembly planning to create intuitive AR interfaces that enhance user understanding of complex tasks. She has also explored dimensionality reduction techniques for general object detection, addressing challenges in video surveillance and digital libraries. By bridging classical feature engineering with practical AR applications, Sharma’s work has laid groundwork for more responsive and context-aware visual systems. Her research continues to inspire advances in how machines perceive and interact with the physical world.