Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Mouse movements of motion-impaired users: a submovement analysis
Assets '04 Proceedings of the 6th international ACM SIGACCESS conference on Computers and accessibility
Universal Access in the Information Society
Developing steady clicks:: a method of cursor assistance for people with motor impairments
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Improvements in vision-based pointer control
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Face as mouse through visual face tracking
Computer Vision and Image Understanding
Hands-free mouse-pointer manipulation using motion-tracking and speech recognition
OZCHI '07 Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces
Designing Computer Interface for Physically Challenged Persons
ICIT '07 Proceedings of the 10th International Conference on Information Technology
ACM Transactions on Accessible Computing (TACCESS)
Hands-free vision-based interface for computer accessibility
Journal of Network and Computer Applications
Experiences using a hands-free interface
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
Fitts' law as a research and design tool in human-computer interaction
Human-Computer Interaction
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Assessment of the use of a human-computer vision interaction framework
HSI'09 Proceedings of the 2nd conference on Human System Interactions
ERCIM'06 Proceedings of the 9th conference on User interfaces for all
Web mediators for accessible browsing
ERCIM'06 Proceedings of the 9th conference on User interfaces for all
Blink and wink detection for mouse pointer control
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Enhanced area cursors: reducing fine pointing demands for people with motor impairments
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Proceedings of the 12th international ACM SIGACCESS conference on Computers and accessibility
HAIL: hierarchical adaptive interface layout
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs: Part I
Computer control by tracking head movements for the disabled
ICCHP'06 Proceedings of the 10th international conference on Computers Helping People with Special Needs
The kernel semi-least squares method for sparse distance approximation
Neural Computation
Personal and Ubiquitous Computing
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Some people cannot use their hands to control a computer mouse due to conditions such as cerebral palsy or multiple sclerosis. For these individuals, there are various mouse-replacement solutions. One approach is to enable them to control the mouse pointer using head motions captured with a web camera. One such system, the Camera Mouse, uses an optical flow approach to track a manually-selected small patch of the subject's face, such as the nostril or the edge of the eyebrow. The optical flow tracker may lose the facial feature when the tracked image patch drifts away from the initially-selected feature or when a user makes a rapid head movement. To address the problem of feature loss, we developed and incorporated the Kernel-Subset-Tracker into the Camera Mouse. The Kernel-Subset-Tracker is an exemplar-based method that uses a training set of representative images to produce online templates for positional tracking. We designed the augmented Camera Mouse so that it can compute these templates in real time, employing kernel techniques traditionally used for classification. We propose three versions of the Kernel-Subset-Tracker, each using a different kernel, and compared their performance to the optical-flow tracker under five different experimental conditions. Our experiments with test subjects show that augmenting the Camera Mouse with the Kernel-Subset-Tracker improves communication bandwidth statistically significantly. Tracking of facial features was accurate, without feature drift, even during rapid head movements and extreme head orientations. We conclude by describing how the Camera Mouse augmented with the Kernel-Subset-Tracker enabled a stroke-victim with severe motion impairments to communicate via an on-screen keyboard.