A cloud-based road infrastructure analysis system was developed to assist as an information management system for detecting and managing information about road defects and road assets. The system uses computer vision and machine learning algorithms and is accessed through a web interface. The detection results are viewed through an online, account-based web interface.

Scapular dyskinesis is a common occurrence in overhead athletes, i.e. athletes who participate in any sport where the upper arm and shoulder is used above the athlete’s head. However, no consensus has been reached on how to evaluate scapular dyskinesis quantitatively. This article describes the development of a measurement technique that can be used to evaluate certain key clinical parameters specific to scapular dyskinesis. The technique employs a 3D structured light computer vision approach to create a surface map of the soft-tissue across the scapula. This surface map is then analyzed using a surface curvature analysis to identify the key clinical parameters associated with scapular dyskinesis. The main advantage of this method is that it provides a marker-less 3D approach. This may aid with diagnosis and monitoring of scapular dyskinesis by allowing measurement data to be collected both before and after treatment and rehabilitation. We expect that this technique will make the monitoring of treatment effectiveness easier while contributing to diagnostic computer vision.