A Vision-Based Approach for Controlling User Interfaces of Mobile Devices

Hannuksela, Jari; Sangi, Pekka; Heikkilä, Janne

doi:10.1109/cvpr.2005.401

Cited by 14 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It contains a 123 MHz ARM-based 32-bit RISC CPU without a floating-point unit, a VGA camera and 3.4 Mb shared memory. The global motion estimation method [2] implemented earlier for this platform was utilized to collect experimental data. The frame rate of this implementation is 10 fps.…”

Section: Methodsmentioning

confidence: 99%

“…A more detailed description can be found in [2]. Global motion refers here to the apparent dominant 2-D motion between frames, which can be approximated by some parameterized flow field model [4,6].…”

Section: Motion Estimationmentioning

confidence: 99%

“…In devices equipped with a camera, successive images obtained from a camera can be used as a source for motion data. Such solutions have been proposed recently [2,3], and they mainly focus on browsing and navigation on the display. However, the motion input combined with pattern recognition techniques can be used for more advanced interaction purposes like recognizing handwriting, gestures and signs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motion-Based Handwriting Recognition for Mobile Interaction

Hannuksela

Sangi

Heikkilä

2006

18th International Conference on Pattern Recognition (ICPR'06)

Self Cite

View full text Add to dashboard Cite

This paper presents a new interaction technique for camera-enabled mobile devices. The handheld device can be used for writing just by moving the device. In our method, interframe dominant motion is estimated from images, and the discrete cosine transform is used for computing discriminating features from motion trajectories. The k-nearest neighbor rule is applied for classification. A realtime implementation of the method was developed for a mobile phone. In experiments, recognition rates ranging from 92 % to 98 % were achieved, which testifies to the practicality of our approach.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Motion Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Motion-Based Handwriting Recognition for Mobile Interaction

Hannuksela

Sangi

Heikkilä

2006

18th International Conference on Pattern Recognition (ICPR'06)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In these methods, feature-based tracking algorithms are performed to analyze the motion of cornerlike features between the consequent frames [5,10]. Another feature-based approach for controlling user interfaces on mobile devices has been proposed by Hannuksela et al, where the motion analysis is performed using a sparse set of features, and a Kalman filter is applied to smooth motion trajectories [9].…”

Section: Related Workmentioning

confidence: 99%

A color-based face tracking algorithm for enhancing interaction with mobile devices

2010

View full text Add to dashboard Cite

A color-based face tracking algorithm is proposed to be used as a human-computer interaction tool on mobile devices. The solution provides a natural means of interaction enabling a motion parallax effect in applications. The algorithm considers the characteristics of mobile useconstrained computational resources and varying environmental conditions. The solution is based on color comparisons and works on images gathered from the front camera of a device. In addition to color comparisons, the coherency of the facial pixels is considered in the algorithm. Several applications are also demonstrated in this work, which use the face position to determine the viewpoint in a virtual scene, or for browsing large images. The accuracy of the system is tested under different environmental conditions such as lighting and background, and the performance of the system is measured in different types of mobile devices. According to these measurements the system allows for accurate (7% RMS error) face tracking in real time (20-100 fps).

show abstract

“…In this method, a feature based tracking algorithm is performed to analyze the motion of corner-like features between the consequent frames [3], [4]. Another feature based approach for controlling user interfaces on mobile devices is developed by Hannuksela et al where motion analysis is performed using a sparse set of features and a Kalman filter is applied to smooth motion trajectories [5].…”

Section: Related Workmentioning

confidence: 99%

A Face Tracking Algorithm for User Interaction in Mobile Devices

Bulbul

Cipiloglu

Çapın

2009

2009 International Conference on CyberWorlds

View full text Add to dashboard Cite

Abstract-A new face tracking algorithm, and a human-computer interaction technique based on this algorithm, are proposed for use on mobile devices. The face tracking algorithm considers the limitations of mobile use case -constrained computational resources and varying environmental conditions. The solution is based on color comparisons and works on images gathered from the front camera of a device. The face tracking system generates 2D face position as an output that can be used for controlling different applications. Two of such applications are also presented in this work; the first example uses face position to determine the viewpoint, and the second example enables an intuitive way of browsing large images.

show abstract

A Vision-Based Approach for Controlling User Interfaces of Mobile Devices

Abstract: We introduce a novel user interface solution for mobile devices

Cited by 14 publications

References 11 publications

Motion-Based Handwriting Recognition for Mobile Interaction

Motion-Based Handwriting Recognition for Mobile Interaction

A color-based face tracking algorithm for enhancing interaction with mobile devices

A Face Tracking Algorithm for User Interaction in Mobile Devices

Contact Info

Product

Resources

About