Faculty are exploring a number of critical problems in the area of computer vision, with a focus on the analysis and modeling of visual scenes from static images as well as video sequences. Research goals include: i) the semantic understanding of materials, objects, and actions within a scene; ii) modeling the spatial organization and layout of the scene and its behavior in time. The algorithms developed in this area of research enable the design of machines that can perform real-world visual tasks such as autonomous navigation, visual surveillance, or content-based image and video indexing.
The frame in which a human marks out the boundaries of an object makes a huge difference in how well AI software can identify that object through the rest of the video.
The most exciting use of AI for me focuses around a better collective use of our available resources, says Corso.
Kyle Min researches how computer vision can analyze law enforcement body cameras.
DARPA is trying to build a system that can turn large data sets into models that can make predictions, and U-M is in on the project.
Centralizing available data in the intelligent systems community through a COmputer Vision Exchange for Data, Annotations and Tools, called COVE.