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Computer Vision & Robotics

List of Faculty

Dr Nazre Batool
Dr Malika Bendechache
Dr Frank Glavin
Dr Josephine Griffith
Dr Karl Mason
Dr James McDermott
Dr Waqar Shahid
Dr Ihsan Ullah

Computer Vision and Robotics are revolutionizing automation by enabling machines to perceive, interpret, and interact with the world. From autonomous navigation to intelligent surveillance, these technologies drive innovation across industries.

Here, you will find information on previous and ongoing projects, the staff and researchers involved, publications, and opportunities in this dynamic field.

Health/Medical	Autonomous Vehicles	Industry 4.0/5.0	Transportation
Agriculture	Robotics	Surveillance Systems

Current Projects

PavAnalytics – https://www.paveanalytics.eu/ – 2023-2025

Lead PI: Dr Ihsan Ullah, Co-PI: Dr Waqar Shahid Qureshi
Description: Visit the website for details.
Funder: Research Ireland (previously Science Foundation Ireland) National Challenge Fund

Augmented Reading Room for Radiologists – 2024-2025

Lead PI: Dr Ihsan Ullah, Co-PI: Dr Eric Wolsztynski (UCC), Collaborators: Dr Aonghus Lawlor (UCD)

Description: The objective of this project is to develop a simple platform that enhances the radiologist’s workflow using AR/VR technology. The proposed “Augmented Reading Room” will enable radiologists to directly interact with research-level AI models without requiring complex or time-consuming integration with hospital PACS systems. This platform is not intended to replace or compete with existing diagnostic tools already available in hospital systems. Instead, it aims to complement them by supporting clinical decision-making and adding a crucial layer of interpretability and explainability, which is often lacking in current AI solutions. Ultimately, the AI4MI “Augmented Reading Room” will facilitate and accelerate the adoption of research-level AI models in the field of radiology.
Funder: Insight Research Ireland Center for Data Analytics

Anatomical Part Segmentation of Newborn Skeleton

PI: Dr Ihasn Ullah - 2024-2025
Description: Accurate and fully articulated modeling of the fetal skeleton plays a crucial role in enhancing childbirth simulations, enabling better prediction of delivery outcomes, and supporting clinical decision-making during labor. However, the collection of high-quality fetal imaging data poses significant challenges due to ethical, technical, and safety constraints. In contrast, neonatal imaging data are more accessible and can serve as a viable proxy for studying and modeling the fetal skeletal structure. This project aims to develop a geometric deep learning framework capable of automatically segmenting and reconstructing the neonatal skeleton from medical imaging data. Ultimately, this work contributes to the broader goal of improving biomechanical simulations of childbirth, facilitating more precise prediction of complications, and paving the way for personalized obstetric care through data-driven modeling of fetal and neonatal anatomy.
Funder: Insight Research Ireland Center for Data Analytics

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