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Outputs (62)

3D Machine vision and deep learning for enabling automated and sustainable assistive physiotherapy (2024)
Presentation / Conference Contribution

The significant beneficial effects of physiotherapy as a treatment for a wide range of medical conditions, is well known. However, the challenge for already stressed healthcare systems to provide effective physiotherapy to an aging population, that h... Read More about 3D Machine vision and deep learning for enabling automated and sustainable assistive physiotherapy.

Maize yield predictive models and mobile-based decision support system for smallholder farmers in Africa (2022)
Presentation / Conference Contribution

Existing machine learning models for crop yield prediction model environmental data on the assumption that soil variables are unaffected by weather variables and therefore learn their intrinsic features independently. If the focus of crop yield predi... Read More about Maize yield predictive models and mobile-based decision support system for smallholder farmers in Africa.

The quiet revolution in machine vision - A state-of-the-art survey paper, including historical review, perspectives, and future directions (2021)
Journal Article

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for tho... Read More about The quiet revolution in machine vision - A state-of-the-art survey paper, including historical review, perspectives, and future directions.

Understanding unconventional preprocessors in deep convolutional neural networks for face identification (2019)
Journal Article

Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, ac... Read More about Understanding unconventional preprocessors in deep convolutional neural networks for face identification.