M
Matthew Turk
Researcher at Toyota Technological Institute at Chicago
Publications - 209
Citations - 33736
Matthew Turk is an academic researcher from Toyota Technological Institute at Chicago. The author has contributed to research in topics: Augmented reality & Facial recognition system. The author has an hindex of 55, co-authored 198 publications receiving 30972 citations. Previous affiliations of Matthew Turk include Massachusetts Institute of Technology & University of California.
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Book ChapterDOI
Flocks of Features for Tracking Articulated Objects
Mathias Kölsch,Matthew Turk +1 more
TL;DR: This chapter presents “Flocks of Features,” a tracking method that combines motion cues and a learned foreground color distribution for fast and robust 2D tracking of highly articulated objects that undergo vast and rapid deformations.
Book ChapterDOI
Recognition of Isolated Fingerspelling Gestures Using Depth Edges
TL;DR: A novel method for recognition of isolated fingerspelling gestures based on depth edge features by using a shift and scale invariant shape descriptor for fingerspelling recognition, demonstrating great improvement over methods that rely on features acquired by traditional edge detection and segmentation algorithms.
Proceedings ArticleDOI
Field-of-view extension for VR viewers
TL;DR: A prototype of a smartphone-based virtual reality (VR) viewer, which can cover nearly the full human field-of-view (FOV) and suggests that such extensions are feasible ways to significantly expand the FOV of standard VR viewers.
Proceedings ArticleDOI
Gaze and head pointing for hands-free text entry: applicability to ultra-small virtual keyboards
TL;DR: To understand how key size reduction affects the accuracy and speed performance of text entry VBIs, a evaluation of gaze-controlled and head-controlled VBI with unconventionally small keys with results that yielded significantly more accurate and fast text production with h-V BI than with g-VBI.
Proceedings ArticleDOI
A projector-camera setup for geometry-invariant frequency demultiplexing
TL;DR: The method is useful to extend the applicability of techniques that rely on the analysis of shadows cast by multiple light sources placed at different positions, as the individual shadows captured at distinct instants of time now can be obtained from a single shot, enabling the processing of dynamic scenes.