October 9th, 12:00 pm- 1:00 pm in DCH 3092
Speaker: Vivek Boominathan (ELEC)
Please indicate interest, especially if you want lunch, here.
Abstract:
Traditional imaging devices capture a photograph, which is then processed through machine learning algorithms to arrive at application-specific inferences such as object detection, tracking, recognition, and others. Thus, the optics in a traditional vision system is entirely unaware of the specifics of post-capture machine learning. In contrast, we suggest that the optical front-end in machine vision systems should be considered and exploited as an additional degree of computational freedom. Incorporating optical design has many benefits, such as improved performance, reduction in physical form factor, low latency, and low power. In this talk, we will dive into the optics design that assists and goes hand in hand with the machine learning task at hand. We will complete the talk by introducing and describing the data-driven approaches to jointly design the optics along with machine vision algorithms/CNN.