PhD Seminar • Computer Vision — Unsupervised Deep Segmentation
Dmitrii Marin, PhD candidate
David R. Cheriton School of Computer Science
Dmitrii Marin, PhD candidate
David R. Cheriton School of Computer Science
Hemant Surale, PhD candidate
David R. Cheriton School of Computer Science
In this seminar, we will present an empirical comparison of eleven barehand, mid-air mode-switching techniques suitable for virtual reality in two experiments.
Ibrahim Kettaneh, Master’s candidate
David. R. Cheriton School of Computer Science
Janneke Ritchie, Founder and CEO
Orange Gate
Hong Zhou, PhD candidate
David R. Cheriton School of Computer Science
Navid Nasr Esfahani, PhD candidate
David R. Cheriton School of Computer Science
Hemant Surale, PhD candidate
David R. Cheriton School of Computer Science
Jayson Lynch, PhD candidate
Massachusetts Institute of Technology
Ji Xin, PhD candidate
David R. Cheriton School of Computer Science
Marcus Brubaker
Department of Electrical Engineering and Computer Science, York University
Research Director, Borealis AI
Modelling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a coarse approximation of real sensor noise.