
Static Scenes with Dynamic Perceptions
Presenter(s)
Kunal Agrawal
Files
Description
In this paper, we explore how computers can recognize motion illusions in static images—pictures that trick our eyes into seeing movement. To study this, we created a new dataset called MISS, which includes images with and without motion illusions. We tested advanced deep learning models to see how well they could identify these illusions and also checked whether color plays an important role. Our results show that these models are good at spotting motion illusions, especially when the images are in color. This highlights the importance of color in helping machines understand motion in still pictures.
Publication Date
4-23-2025
Project Designation
Graduate Research
Primary Advisor
Tam Nguyen
Primary Advisor's Department
Computer Science
Keywords
Stander Symposium, College of Arts and Sciences
Institutional Learning Goals
Community; Diversity; Scholarship
Recommended Citation
"Static Scenes with Dynamic Perceptions" (2025). Stander Symposium Projects. 4137.
https://ecommons.udayton.edu/stander_posters/4137

Comments
10:20-10:40, LTC Studio