Samundra Deep

University of Electronic Science and Technology of China

Papers

Total Citations

H-Index

About

Samundra Deep is a researcher at the intersection of computer vision, robotics, and intelligent systems, with a particular focus on enabling machines to perceive and interpret complex natural environments. His most cited work, "Vision prehension with CBIR for cloud robo" (2014), addresses the challenging problem of arbitrary scene understanding by integrating Content-Based Image Retrieval (CBIR) with neural network architectures. This paper, which has garnered 8 citations, proposes a novel framework that allows cloud-connected robots to grasp visual context from unstructured scenes—a critical step toward more autonomous and adaptive robotic systems. Deep’s contributions lie in fusing image retrieval techniques with neural computation to enhance visual perception, laying groundwork for cloud robotics applications. His research speaks to the growing need for robust, real-time visual processing in dynamic settings, and his work continues to inform advances in robotic vision and intelligent image analysis.