Murtaza Taj

Lahore University of Management Sciences, University of London

Papers

Total Citations

H-Index

About

Murtaza Taj is a computer vision researcher whose work bridges theoretical innovation and practical application, with a focus on camera calibration, 3D reconstruction, and automated systems for education. His most-cited paper, "Camera Calibration Through Camera Projection Loss" (2022, 12 citations), introduces a novel method for predicting both extrinsic and intrinsic camera parameters—such as baseline, pitch, focal length, and principal point offset—using a projection loss framework. This work addresses critical challenges in robotics, autonomous driving, and 3D reconstruction, offering a more robust approach to a fundamental computer vision task. Earlier in his career, Taj contributed to educational technology with "Hands-On Experience in Image Processing: The Automated Lecture Cameraman" (2007, 2 citations), a student-built system deployed in real distance learning scenarios that automated lecture recording. This project exemplifies his commitment to hands-on, deployable solutions. While his citation counts are modest, Taj’s work demonstrates a clear trajectory from practical educational tools to foundational camera calibration research, making him a researcher to watch in the intersection of computer vision and real-world systems.