Andrew M. Taylor

Ashild Kummen, Guanlin Li, Ali Hassan et al.

Touchless computer interaction has become an important consideration during the COVID-19 pandemic period. Despite progress in machine learning and computer vision that allows for advanced gesture recognition, an integrated collection of such open-source methods and a user-customisable approach to utilising them in a low-cost solution for touchless interaction in existing software is still missing. In this paper, we introduce the MotionInput v2.0 application. This application utilises published open-source libraries and additional gesture definitions developed to take the video stream from a standard RGB webcam as input. It then maps human motion gestures to input operations for existing applications and games. The user can choose their own preferred way of interacting from a series of motion types, including single and bi-modal hand gesturing, full-body repetitive or extremities-based exercises, head and facial movements, eye tracking, and combinations of the above. We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation. Three use case areas assisted the development of the modules: creativity software, office and clinical software, and gaming software. A collection of open-source libraries has been integrated and provide a layer of modular gesture mapping on top of existing mouse and keyboard controls in Windows via DirectX. With ease of access to webcams integrated into most laptops and desktop computers, touchless computing becomes more available with MotionInput v2.0, in a federated and locally processed method.

Maria A. Zuluaga, Alex F. Mendelson, M. Jorge Cardoso et al.

One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. In this work, we exploit the segmentation errors associated with poor atlas selection to build a computer aided diagnosis (CAD) system for pathological classification in post-operative dextro-transposition of the great arteries (d-TGA). The proposed approach extracts a set of features, which describe the quality of a segmentation, and introduces them into a logical decision tree that provides the final diagnosis. We have validated our method on a set of 60 whole heart MR images containing healthy cases and two different forms of post-operative d-TGA. The reported overall CAD system accuracy was of 93.33%.