How human vision separately determines object and scene motion. This project aims to enhance understanding of how people process visual scenes containing multiple moving objects of interest. The project intends to measure human visual performance to determine how the brain processes multiple motion signals simultaneously. Expected outcomes include an increased understanding of how we are able to use an evolving visual scene to distinguish between changes due to self-motion and those due to the m ....How human vision separately determines object and scene motion. This project aims to enhance understanding of how people process visual scenes containing multiple moving objects of interest. The project intends to measure human visual performance to determine how the brain processes multiple motion signals simultaneously. Expected outcomes include an increased understanding of how we are able to use an evolving visual scene to distinguish between changes due to self-motion and those due to the motion of multiple moving objects such as crowded city footpaths and busy roads. The results will improve our understanding of failures to see moving objects in challenging viewing conditions (for example, high density traffic), and inform work in the design of autonomous driving and augmented reality display systems.Read moreRead less
Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human ....Using shape change for object perception: human and artificial vision. This project aims to examine the steps taken by the visual system to code the shape of objects, including those that change shape over time. The project seeks to employ experiments assessing human vision and machine learning techniques to examine these codes and, in particular, focus on the advantages of a system that exaggerates shape change over time. Expected outcomes include an improved shape code based on superior human performance that can have many applications in automated visual systems. This project can directly benefit the animation industries where the creation of realistic movement of humans and animals remains a computationally intensive challenge.Read moreRead less