Branislav Kisačanin & Vladimir Pavlović & Thomas S. Huang <Real-Time Vision for Human-Computer Interaction>

Branislav Kisačanin & Vladimir Pavlović & Thomas S. Huang
Real-Time Vision for Human-Computer Interaction
(RTV4HCI)
Springer, 2005
(google book's overview)

2004 IEEE CVPR Workshop on RTV4HCI - Papers
http://rtv4hci.rutgers.edu/04/

Computer vision and pattern recognition continue to play a dominant role in the HCI realm. However, computer vision methods often fail to become pervasive in the field due to the lack of real-time, robust algorithms, and novel and convincing applications.

Keywords:

head and face modeling
map building
pervasive computing
real-time detection

Contents:

RTV4HCI: A Historical Overview.
- Real-Time Algorithms: From Signal Processing to Computer Vision.
- Recognition of Isolated Fingerspelling Gestures Using Depth Edges.
- Appearance-Based Real-Time Understanding of Gestures Using Projected Euler Angles.
- Flocks of Features for Tracking Articulated Objects.
- Static Hand Posture Recognition Based on Okapi-Chamfer Matching.
- Visual Modeling of Dynamic Gestures Using 3D Appearance and Motion Features.
- Head and Facial Animation Tracking Using Appearance-Adaptive Models and Particle Filters.
- A Real-Time Vision Interface Based on Gaze Detection -- EyeKeys.
- Map Building from Human-Computer Interactions.
- Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures.
- Epipolar Constrained User Pushbutton Selection in Projected Interfaces.
- Vision-Based HCI Applications.
- The Office of the Past.
- MPEG-4 Face and Body Animation Coding Applied to HCI.
- Multimodal Human-Computer Interaction.
- Smart Camera Systems Technology Roadmap.
- Index.

RTV4HCI: A Historical Overview

TurkOverviewHCI05.pdf

Matthew Turk (mturk@cs.ucsb.edu)
University of California, Santa Barbara
http://www.stanford.edu/~mturk/
http://www.cs.ucsb.edu/~mturk/

The goal of research in real-time vision for human-computer interaction is to develop algorithms and systems that sense and perceive humans and human activity, in order to enable more natural, powerful, and effective computer interfaces.

Computers in the Human Interaction Loop (CHIL)

perceptual interfaces
multimodal interfaces
post-WIMP(windows, icons, menus, pointer) interfaces

implicit user awareness or explicit user control

The user interface
- the software and devices that implement a particular model (or set of models) of HCI

Computer vision technologies must ultimately deliver a better "user experience".

B Shneiderman, Designing the User Interface: Strategies for Effective Human-Computer Interaction, Third Edition, Addison-Wesley, 1998.
: 1) time to learn 2) speed of performance 3) user error rates 4) retention over time 5) subjective satisfaction

- Presence and location (Face and body detection, head and body tracking)
- Identity (Face recognition, gait recognition)
- Expression (Facial feature tracking, expression modeling and analysis)
- Focus of attention (Head/face tracking, eye gaze tracking)
- Body posture and movement (Body modeling and tracking)
- Gesture (Gesture recognition, hand tracking)
- Activity (Analysis of body movement)

eg.
VIDEOPLACE (M W Krueger, Artificial Reality II, Addison-Wesley, 1991)
Magic Morphin Mirror / Mass Hallucinations (T Darrell et al., SIGGRAPH Visual Proc, 1997)

Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Gabor Wavelet Networks (GWNs)
Active Appearance Models (AAMs)
Hidden Markov Models (HMMs)

Identix Inc.
Viisage Technology Inc.
Cognitec Systems


- MIT Medial Lab
ALIVE system (P Maes et al., The ALIVE system: wireless, full-body interaction with autonomous agents, ACM Multimedia Systems, 1996)
PFinder system (C R Wren et al., Pfinder: Real-time tracking of the human body, IEEE Trans PAMI, pp 780-785, 1997)
KidsRoom project (A Bobick et al., The KidsRoom: A perceptually-based interactive and immersive story environment, PRESENCE: Teleoperators and Virtual Environments, pp 367-391, 1999)

Flocks of Features for Tracking Articulated Objects

KolschBook05.pdf

Mathias Kolsch (kolsch@nps.edu
Computer Science Department, Naval Postgraduate School, Monterey
Matthew Turk (mturk@cs.ucsb.edu)
Computer Science Department, University of California, Santa Barbara

flocking behavior

http://www.movesinstitute.org/~kolsch/HandVu/HandVu.html

Visual Modeling of Dynamic Gestures Using 3D Appearance and Motion Features

Visual Modeling of Dynamic Gestures Using 3D Appearance and.pdf

Guangqi Ye (grant@cs.jhu.edu), Jason J. Corso, Gregory D. Hager
Computational Interaction and Robotics Laboratory
The Johns Hopkins University



Map Building from Human-Computer Interactions

arsenioCVPRWorkshop.pdf

http://groups.csail.mit.edu/lbr/mars/pubs/pubs.html#publications
Artur M. Arsenio (arsenio@csail.mit.edu)
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology



Vision-Based HCI Applications
Eric Petajan (eric@f2f-inc.com)
face2face animation, inc.
eric@f2f-inc.com



The Office of the Past

kim04office.pdf

Jiwon Kim (jwkim@cs.washington.edu), Steven M. Seitz (seitz@cs.washington.edu)
University of Washington
Maneesh Agrawala (maneesh@microsoft.com)
Microsoft Research
Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10  Page: 157   Year of Publication: 2004
http://desktop.google.com
http://grail.cs.washington.edu/projects/office/
http://www.realvnc.com/



Smart Camera Systems Technology Roadmap
Bruce Flinchbaugh (b-flinchbaugh@ti.com)
Texas Instruments