{"id":126,"date":"2021-08-09T23:43:42","date_gmt":"2021-08-09T23:43:42","guid":{"rendered":"http:\/\/wp.lancs.ac.uk\/icvl\/?page_id=126"},"modified":"2025-07-23T12:44:54","modified_gmt":"2025-07-23T12:44:54","slug":"research","status":"publish","type":"page","link":"http:\/\/wp.lancs.ac.uk\/icvl\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\n

Our work crosses many domains including the development of novel machine\/ deep learning methodology, solution techniques, pose estimation, medical imaging and forensic identification.<\/p>\n\n\n\n

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Biometrics and Forensics<\/a><\/div>\n\n\n\n
Medical Imaging<\/a><\/div>\n\n\n\n
Computer Vision<\/a><\/div>\n\n\n\n
Machine Learning<\/a><\/div>\n\n\n\n
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Biometrics and Forensic Identification<\/h3>\n\n\n\n
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Our work focuses on the development of computer vision techniques for the forensic identification of individuals and criminal evidence, primary through photo and video. This multidisciplinary work includes identification through and the study of anatomical variability, age estimation, scene comparison, localisation and transcription of text.<\/p>\n\n\n\n

Much of our research is in the advancement of computer vision and deep learning-based theory and methodology for the forensic identification of the perpetrators of child sexual abuse (CSA) from photographs and video footage through the development of novel biometrics and exploration of novel biometric traits. This research has been funded by ERC and EPSRC, and published in the leading publications including CVPR, ECCV, ICCV and Pattern Recognition. Over 80% of CSA victims are female and the World Health Organisation estimates that 18% of females and 7.6% of males have been subjected to sexual abuse before the age of 18. Disabled children are approximately 3.1x more likely to be abused. We are working to strengthen the evidence to support investigations of CSA, accelerate the number of CSA cases that can be investigated, and significantly improve capabilities in suspect determination.<\/p>\n<\/div>\n\n\n\n

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H-Unique<\/p>\n<\/div>\n\n\n\n

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Large Scale Identification in Natural Scenes<\/p>\n<\/div>\n\n\n\n

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Optical Character Recognition<\/p>\n<\/div>\n\n\n\n

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Medical Imaging<\/h3>\n\n\n\n
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Our work in medical imaging focuses on the development of computer vision techniques to automatically analyse and interpret medical images, including tomography and pre-clinical imaging. We have a strong focus on pathology extraction and measurement, diagnosis and technology development in ophthalmic imaging, cancer and COVID-19.<\/p>\n<\/div>\n\n\n\n

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Glaucoma Diagnosis<\/p>\n<\/div>\n\n\n\n

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Breast Cancer<\/p>\n<\/div>\n\n\n\n

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COVID-19 Prognosis<\/p>\n<\/div>\n\n\n\n

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Computer Vision<\/h3>\n\n\n\n
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Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs in order to take actions or make recommendations based on that information. Much of our work is centred on the development of computer vision methodology in diverse areas, including pose estimation, pharmaceutical manufacturing, and environmental science.<\/p>\n<\/div>\n\n\n\n

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Pose Estimation<\/p>\n<\/div>\n\n\n\n

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Pharmaceutical Manufacturing<\/p>\n<\/div>\n\n\n\n

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Environmental Science<\/p>\n<\/div>\n\n\n\n

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Machine Learning<\/h3>\n\n\n\n
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Machine learning (ML) is a subfield of AI that is devoted to understanding and building methods that are capable of learning, i.e. they can leverage data to improve performance on a task or set of tasks. Our work in machine learning focuses on algorithm development, efficient methodology, and numerical solution techniques.<\/p>\n<\/div>\n\n\n\n

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Deep Learning<\/p>\n<\/div>\n\n\n\n

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Image Segmentation<\/p>\n<\/div>\n\n\n\n

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Classification<\/p>\n<\/div>\n\n\n\n

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Publications<\/h3>\n\n\n\n
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2022<\/h3>\n\n\n\n
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