Riaz Ahmed Shaikh

University of Electronic Science and Technology of China

Papers

Total Citations

H-Index

About

Riaz Ahmed Shaikh is a researcher whose work sits at the intersection of computer vision, image processing, and robotics, with a particular focus on enabling machines to perceive and understand complex natural environments. His most cited paper, "Vision prehension with CBIR for cloud robo" (2014), tackles the challenging problem of arbitrary scene perception by integrating Content-Based Image Retrieval (CBIR) with neural network architectures. This work proposes a framework that allows robots to leverage cloud-based image retrieval to interpret visual data, addressing a critical bottleneck in autonomous robotic vision. With 8 citations, this paper has contributed to the growing dialogue on cloud robotics and visual cognition. Shaikh’s research is notable for its practical approach to fusing AI-driven image analysis with robotic prehension—the act of grasping or interacting with objects based on visual input. His contributions are particularly relevant for students and researchers exploring the convergence of deep learning, CBIR, and robotic perception, offering a foundation for building more adaptive and context-aware autonomous systems.