Daniel MorrisAssociate Professor
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Research
What is exciting right now is solving computer vision problems using machine learning and artificial intelligence techniques. Tasks that seemed way beyond reach a decade ago have been solved! But there are still many problems to needing solutions. Here are some that I am working on.
Smart Agriculture, I believe, is the next big frontier for automation. We are exploring automated phenotyping and segmentation in cluttered scenes, as well as posture estimation of animals in depth images. I am actively involved in MSU NRT-IMPACTS, a program seeking to bridge the gap between computational and plant sciences through mentoring, teaching and research.
Automated Vehicles need vision to function safely and reliably. My students and I are finding ways to fuse video and lidar to obtain accurate, high-resolution depth maps that do not smear along the boundaries. We're also solving problems in radar-video fusion, tracking and object classification.
Tremor Tracking promises to enable contactless monitoring devices for Parkinson's Patients. Our recent work extends the Kinect 2 to accurately measure small-amplitude tremors.
Teaching
ECE 201: Circuits and Systems I
Circuits are fun and this course is a great way to learn the basics of analyzing linear circuits. Topics include Ohm's law, Kirchhoff's circuit laws, superposition, Thévenin and Norton equivalent circuits, Node Voltage Analysis, Mesh Current Analysis, as well as first and second order circuit responses.
ECE-CSE 434: Autonomous Vehicles
We are on the cusp of a transportation revolution as our vehicles will soon take over the task navigating us to where we want to go, and save countless lives in the process. The Autonomous Vehicles class is a chance to learn all about these advances and to implement key technologies yourself.
More course details are here.