Dena Bazazian

University of Plymouth, University of Bristol

Papers

Total Citations

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About

Dena Bazazian is a researcher whose work bridges computer vision, robotics, and perceptual computing. Her primary research areas include 3D reconstruction for autonomous systems and computational shape analysis. Bazazian’s major contribution lies in advancing how robots perceive and interact with their environments through robust 3D reconstruction techniques, as detailed in her highly cited 2025 review, "3D Reconstruction in Robotics: A Comprehensive Review" (6 citations), which synthesizes critical developments in the field. She also explores the intersection of human perception and machine vision in her 2022 work, "Perceptually grounded quantification of 2D shape complexity" (3 citations), where she introduces novel metrics for evaluating shape complexity that align with human visual intuition. This work has implications for object recognition, design, and AI-driven visual systems. Bazazian’s research not only pushes the boundaries of robotic autonomy but also deepens our understanding of how computational models can mirror human perceptual processes. Her contributions are shaping the next generation of intelligent, perceptually aware machines.