Tony Pridmore is Professor and from January 1st 2024 Head of Computer Science at the University of Nottingham. He holds a BSc in Computer Science from the University of Warwick and a PhD in Computer Vision from the University of Sheffield. Before returning to Computer Science in 1999 he held academic posts in both Information and Manufacturing Engineering.
Tony's current work is directed towards the development of integrated plant phenomics technologies. He is Director of PhenomUK, the UK national Plant Phenotyping network and Chairs the Computer Vision and Artificial Intelligence Working Group of the International Plant Phenotyping Network (IPPN). Tony is co-founder and a member of the management board of the Hounsfield Facility, a unique installation providing automated extraction of 3D structural descriptions of plants from X-ray data. Tony serves on the International Scientific Advisory Committee of the University of Saskatchewan's Plant Phenotyping and Imaging Research Centre (P2IRC) and the German Cluster of Excellence PhenoRob. He is Associate Editor of the Journal Plant Phenomics.
My research interests centre on image analysis and computer vision, particularly 3D reconstruction, visual tracking and their application to bioimage analysis and image-based phenotyping. I am… read more
My research interests centre on image analysis and computer vision, particularly 3D reconstruction, visual tracking and their application to bioimage analysis and image-based phenotyping. I am particularly interested in
i) model-based, or dynamic, phenotyping, in which camera a sensor placement is determined by measurements already made, and so tuned to the individual plant.
ii) the recovery of descriptions of the 3D growth of plant components. For roots this can be achieved via analysis of time-series X-ray micro-computed tomography images
iii) the role of deep machine learning methods in plant phenotyping
iv) the establishment and maintenance of large scale, distributed research infrastructures making plant phenotyping facilities available nationally and internationally.
Previous work has addressed issues in binocular stereo, knowledge-based image interpretation (particularly the interpretation of images of mechanical and other drawings), camera pose/motion recovery and optic flow in the context of static and mobile robots. All my work has mixed the development of novel methods with their application to real-world problems.