Thomas Kinsman is interested in Pattern Recognition and its application to Human Vision and Computer Vision. To that end, Thomas studies perceptual distance metrics for human perception, data visualization, and machine learning. Thomas has years of experience in the Kodak Research labs, and has expertise in performance optimization, and robust software design.
Tommy P. Keane is a third year Ph.D. student in the Center for Imaging Science. He received his dual-degree B.S./M.S. in Electrical Engineering from RIT while working with Dr. Eli Saber on automated image registration for surveillance cameras. He is currently working with Dr. Nathan Cahill of RIT’s School of Mathematical Sciences and Dr. Jeff Pelz of the Center for Imaging Science. Tommy works in the MVRL on the capture, processing, and display of digital photo and video imagery of indoor and outdoor scenes. His dissertation research in Computer Vision focuses on the integration of said imagery with eyetracking and GPS data for geological study, virtual field trips, advanced data visualizations, multidimensional statistical signal processing, and developing smooth manifold learning techniques. Tommy has won the Brady Prize Essay Contest at the ICVSS 2012, and was the recipient of the Best Student Paper award at the IEEE WNYIPW 2012.
Preethi Vaidyanathan is a 5th year doctoral student in Chester F. Carlson Center for Imaging Science working under the supervision of Drs. Jeff Pelz, Anne Haake and Cecilia Alm at the Multidisciplinary Vision Research Lab. She received her Masters degree in Electrical Engineering from Rochester Institute of Technology in 2009. Her interests include eye-tracking, computer vision, multimodal data integration, machine learning. Her research aims at understanding images at semantic level and develop sophisticated image information systems. Currently she is working on developing a novel method of high-level image understanding via multimodal data integration.
Her research interest is eye tracking data quality standardization and eye tracking related image processing. Data quality is an important issue eye movement research because reliable data is the foundation of reliable analysis and conclusions. The main aspects of data quality include spatial accuracy, precision and temporal latency of the eye tracking system. In this project, besides calibration for every participant, she also puts a triggering area of interest (tAOI) before each stimulus to calculate accuracy though the whole experiment, which serves as a reference for validity check. To improve the tracking accuracy, she has done a project compensating for tracking error caused by head motion velocity of a remote eye tracker. As a PhD student in imaging science. she has also explored the bottom up feature extraction of the dermatological images and feature evaluation based on eye movement data.
I'm a first year PhD student working in the Virtual Reality lab (PerForM). I did my BsC/MsC Degrees in Electronics engineering. My research interest is studying human visual system and motion perception/prediction using Virtual Reality tools. Currently I'm working on a method to measure and minimize temporal latency and calibrate/compensate for the spatial errors in a VR environment.