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Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production.
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Bear, Helen and Harvey, Richard ORCID: https://orcid.org/0000-0001-9925-8316
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Alternative visual units for an optimized phoneme-based lipreading system.
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Bird, Daniel and Laycock, Stephen (2019) GPGPU acceleration of environmental and movement datasets. In: ACM SIGGRAPH 2019 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2019, 2019-07-28.
Chin, Jeannette ORCID: https://orcid.org/0000-0002-9398-5579, Callaghan, Vic and Allouch, Somaya Ben
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The Internet of Things: Reflections on the past, present and future from a user centered and smart environment perspective.
Journal of Ambient Intelligence and Smart Environments, 11 (1).
pp. 45-69.
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Dorgham, Osama, Al-Mherat, Ibrahim, Al-Shaer, Jawdat, Bani-Ahmad, Sulieman and Laycock, Stephen (2019) Smart system for prediction of accurate surface electromyography signals using an artificial neural network. Future Internet, 11 (1). ISSN 1999-5903
Finlayson, Graham, Gong, Han and Fisher, Robert (2019) Color homography: Theory and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (1). pp. 20-33. ISSN 0162-8828
Finlayson, Graham and Zhu, Yuteng (2019) Finding a Colour Filter to Make a Camera Colorimetric by Optimisation. In: Computational Color Imaging. Lectures Notes in Computer Science . Springer, pp. 53-62. ISBN 978-3-030-13939-1
Gong, Han, Finlayson, Graham, Fisher, Robert and Fang, Fufu (2019) 3D color homography model for photo-realistic color transfer re-coding. The Visual Computer, 35 (3). 323–333. ISSN 0178-2789
Hemrit, Ghalia, Finlayson, Graham David, Gijsenij, Arjan, Gehler, Peter Vincent, Bianco, Simone, Drew, Mark, Funt, Brian and Shi, Lilong (2019) Providing a single ground-truth for illuminant estimation for the ColorChecker dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (8). pp. 1-3. ISSN 0162-8828
Hemrit, Ghalia, Matsushita, Futa, Uchida, Mihiro, Vasqez-Corral, Javier, Gong, Han, Tsumura, Norimichi and Finlayson, Graham (2019) Using the Monge-Kantorovitch Transform in Chromagenic Color Constancy for Pathophysiology. In: Computational Color Imaging. Lecture Notes in Computer Science . Springer, pp. 121-133. ISBN 978-3-030-13939-1
Lapeer, Rudy, Gerikhanov, Zelimkhan, Sadulaev, Said-Magomed, Audinis, Vilius, Rowland, Roger, Crozier, Kenda ORCID: https://orcid.org/0000-0002-5328-9376 and Morris, Edward
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A computer‑based simulation of childbirth using the partial Dirichlet–Neumann contact method with total Lagrangian explicit dynamics on the GPU.
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Mackiewicz, Michal ORCID: https://orcid.org/0000-0002-8777-8880, Rivertz, Hans Jakob and Finlayson, Graham
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Spherical sampling methods for the calculation of metamer mismatch volumes.
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Matthews, Nick, Kitao, Akio, Laycock, Stephen and Hayward, Steven ORCID: https://orcid.org/0000-0001-6959-2604
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Haptic-assisted interactive molecular docking incorporating receptor flexibility.
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Newman, Jacob, Phillips, John and Cox, Stephen (2019) CAVA: A System for Identifying Nystagmus. In: IEEE Conference on Biomedical and Health Informatics 2019. UNSPECIFIED.
Newman, Jacob L., Phillips, John S., Cox, Stephen J., Fitzgerald, John and Bath, Andrew (2019) Automatic nystagmus detection and quantification in long-term continuous eye-movement data. Computers in Biology and Medicine, 114. ISSN 0010-4825
Phillips, John S., Newman, Jacob L. and Cox, Stephen J. (2019) An investigation into the diagnostic accuracy, reliability, acceptability and safety of a novel device for Continuous Ambulatory Vestibular Assessment (CAVA). Scientific Reports, 9. ISSN 2045-2322
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Vasqez-Corral, Javier, Finlayson, Graham and Bertalmio, Marcelo (2019) Physically Plausible Dehazing for Non-physical Dehazing Algorithms. In: Computational Color Imaging. Lecture Notes in Computer Science . Springer, pp. 233-244. ISBN 978-3-030-13939-1
Vickers, William and Milner, Ben (2019) Reducing processing requirements for right whale detection from autonomous surface vehicles. In: UNSPECIFIED.
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A comparison of machine learning methods for detecting right whales from autonomous surface vehicles.
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Detecting right whales from autonomous surface vehicles using RNNs and CNNs.
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