Single-camera dedicated television control system using gesture drawing

Jeong, Soonmook; Jin, Jungdong; Song, Tae Houn; Kwon, Key-Ho; Jeon, Jae Wook

doi:10.1109/tce.2012.6414977

Cited by 32 publications

(9 citation statements)

References 17 publications

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“…If the threshold value is too small, it decreases the inhibitory effect of the blurring effect. In addition, it is also very important to choose appropriate size, shape and weighted values in a template because that can overcome the simple local averaging effectively (Jeong, Jin, Song, Kwon & Jeon, 2012).…”

Section: Neighborhood Averaging Methodsmentioning

confidence: 99%

A virtual keyboard implementation based on finger recognition

Zhang

Yan

Narayanan

2017

2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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A keyboard requires a great deal of resources and is restricted by its physical features. Additionally, discarded keyboards also inevitably contribute to environmental pollution. Consequently, the touch screen is designed to replace the physical keyboard and thus reduce these flaws. However, the internal digital keyboard on the touch screen takes up a substantial amount of space, which causes some content to be covered. Moreover, the touch screen can be dirtied by fingerprints and become worn over time by human fingernails through frequent use. Hence, it is necessary to develop a new type of environment-friendly virtual keyboard with fewer flaws. In this thesis, the author develops a novel type of virtual keyboard that allows users to use fingers to type on a piece of paper at any fixed plane. Finger recognition is based on human skin tone and then the BWMORPH algorithm is utilized to recognize the user's fingertip. If the user's fingertip has remained on a key for a long time, the program will regard this key as an input. The experiments in this thesis adopt five kinds of customized paper keyboards on a wall to demonstrate the usability of the proposed virtual keyboard. Typing without touching the keyboard is fulfilled to ignore obstrutions covering the paper keyboard. The experiment results indicate that the overall recognition rate of the proposed virtual keyboard is 94.62%. The proposed virtual keyboard can be put to use for a smartphone in the future. Furthermore, as a blueprint, it can be applied to computers after it is improved to allow ten-finger recognition. Moreover, machine learning can be potentially embedded into our virtual keyboard so as to greatly improve its finger recognition performance.

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Section: Neighborhood Averaging Methodsmentioning

confidence: 99%

A virtual keyboard implementation based on finger recognition

Zhang

Yan

Narayanan

2017

2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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“…Freeman and Weissman were first to explore the idea of controlling a television using hand gestures over 20 years ago [10], observing fatigue issues now often referred to as "gorilla arm" [12,13]. More recent research on the topic has largely focussed on library-based gestural techniques [6,16,17] and highlighted issues with learning and remembering of gestures and gesture-to-function mappings [31]. TraceMatch avoids these issues, as the movements employed as input are small and periodic, and guided by the visual display of animated controls.…”

Section: Touchless Gestural Interactionmentioning

confidence: 99%

Remote Control by Body Movement in Synchrony with Orbiting Widgets

Clarke

Bellino

Esteves

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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In this work we consider how users can use body movement for remote control with minimal effort and maximum flexibility. TraceMatch is a novel technique where the interface displays available controls as circular widgets with orbiting targets, and where users can trigger a control by mimicking the displayed motion. The technique uses computer vision to detect circular motion as a uniform type of input, but is highly appropriable as users can produce matching motion with any part of their body. We present three studies that investigate input performance with different parts of the body, user preferences, and spontaneous choice of movements for input in realistic application scenarios. The results show that users can provide effective input with their head, hands and while holding objects, that multiple controls can be effectively distinguished by the difference in presented phase and direction of movement, and that users choose and switch modes of input seamlessly. CCS Concepts: • Human-centered computing → Interaction devices; Gestural input; Ubiquitous and mobile computing;

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“…Conventionally, a TV remote control is shared, and it must be passed from person to person. Gesture control for TV has been investigated in prior research, driven by users' desire to have instant control [40], but this has primarily focussed on librarybased gestural techniques [14,31,33]. The level of interaction with TVs is increasing with the integration of "smart" features, while users primarily focus on the content displayed, or may watch it in the background whilst performing other tasks with minimal interaction.…”

Section: Tv Remote Controlmentioning

confidence: 99%

MatchPoint

Clarke

Gellersen

2017

Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology

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Figure 1. Spontaneous spatial coupling is a hybrid technique of motion-matching and pointing. Controls in the form of moving targets are presented to the user (A). When the user synchronises their movement with a target (B), a spatial coupling is created between the input modality (C) and the control (D). The technique enables ad hoc appropriation of any part of their body, or any object they hold, as a pointing device.

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Single-camera dedicated television control system using gesture drawing

Cited by 32 publications

References 17 publications

A virtual keyboard implementation based on finger recognition

A virtual keyboard implementation based on finger recognition

Remote Control by Body Movement in Synchrony with Orbiting Widgets

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