Vision-based motion estimation for interaction with mobile devices

Hannuksela, Jari; Sangi, Pekka; Heikkilä, Janne

doi:10.1016/j.cviu.2006.10.014

Cited by 27 publications

(18 citation statements)

References 9 publications

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“…According to [33], the best architecture is a superscalar, superpipelined structure. This design strategy has been adopted and the resulting system allows our system to be used as an embedded coprocessor in the resolution of other problems, for inclusion in mobile devices such as the one described in [34] for instance. In Fig.…”

Section: From Coarse To Superpipeline Architecturementioning

confidence: 99%

Superpipelined high-performance optical-flow computation architecture

Díaz

Ros

Agís

et al. 2008

Computer Vision and Image Understanding

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Section: From Coarse To Superpipeline Architecturementioning

confidence: 99%

Superpipelined high-performance optical-flow computation architecture

Díaz

Ros

Agís

et al. 2008

Computer Vision and Image Understanding

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“…This movement is generally mapped to the pointer displacement as an alternative to a mouse or a touch screen. Since most mobile devices have a single camera, the movement is typically tracked using feature-based algorithms such as optical flow and marker tracking (Haro et al, 2005;Wang et al, 2006;Hannuksela et al, 2007;, instead of homography induced from multiple views. However, feature-based tracking can suffer from a lack of robust features or error accumulation (Barron et al, 1994), leading to the need for more sensors for accurate tracking.…”

Section: Motion-sensing Interfaces For Mobile Devicesmentioning

confidence: 99%

Evaluation of motion-based interaction for mobile devices: A case study on image browsing

Yim

Lee

Choi

2011

Interacting with Computers

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a b s t r a c tThis article evaluates the usability of motion sensing-based interaction on a mobile platform using image browsing as a representative task. Three types of interfaces, a physical button interface, a motion-sensing interface using a high-precision commercial 3D motion tracker, and a motion-sensing interface using an in-house low-cost 3D motion tracker, are compared in terms of task performance and subjective preference. Participants were provided with prolonged training over 20 days, in order to compensate for the participants' unfamiliarity with the motion-sensing interfaces. Experimental results showed that the participants' task performance and subjective preference for the two motion-sensing interfaces were initially low, but they rapidly improved with training and soon approached the level of the button interface. Furthermore, a recall test, which was conducted 4 weeks later, demonstrated that the usability gains were well retained in spite of the long time gap between uses. Overall, these findings highlight the potential of motion-based interaction as an intuitive interface for mobile devices.

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“…5. For details, please see the paper by Hannuksela et al 8 The blocks in the top left present the selected image regions to be used, while the lines in the top right image illustrate the block displacement estimates, d, and ellipses show the related uncertainties. The bottom left image shows the trusted features that are used for parametric model fitting.…”

Section: Motion Based User Interfacementioning

confidence: 99%

“…8 Our approach utilises the feature based motion analysis where a sparse set of blocks are first selected from one image and then their displacements are determined. In order to improve the accuracy of the motion information, an uncertainty of these features is also analysed.…”

Section: Motion Based User Interfacementioning

confidence: 99%

New video applications on mobile communication devices

Silvén

Hannuksela

López

et al. 2008

Multimedia on Mobile Devices 2008

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The video applications on mobile communication devices have usually been designed for content creation, access, and playback. For instance, many recent mobile devices replicate the functionalities of portable video cameras and video recorders, and digital TV receivers. These are all demanding uses, but nothing new from the consumer point of view. However, many of the current devices have two cameras built in, one for capturing high resolution images, and the other for lower, typically VGA (640x480 pixels) resolution video telephony. We employ video to enable new applications and describe four actual solutions implemented on mobile communication devices. The first one is a real-time motion based user interface that can be used for browsing large images or documents such as maps on small screens. The motion information is extracted from the image sequence captured by the camera. The second solution is a real-time panorama builder, while the third one assembles document panoramas, both from individual video frames. The fourth solution is a real-time face and eye detector. It provides another type of foundation for motion based user interfaces as knowledge of presence and motion of a human faces in the view of the camera can be a powerful application enabler.

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Vision-based motion estimation for interaction with mobile devices

Cited by 27 publications

References 9 publications

Superpipelined high-performance optical-flow computation architecture

Superpipelined high-performance optical-flow computation architecture

Evaluation of motion-based interaction for mobile devices: A case study on image browsing

New video applications on mobile communication devices

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