Target acquisition with camera phones when used as magic lenses

Rohs, Michael; Oulasvirta, Antti

doi:10.1145/1357054.1357275

Cited by 58 publications

(70 citation statements)

References 22 publications

(28 reference statements)

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“…With such settings, pointing is usually performed with either a screen-center crosshair [8,11,[14][15][16] or by direct input on the screen, using a pen or bare fingers [4,[8][9][10][11]18]. Rohs et al [14,15] studied pointing with a screen-centered crosshair on a phone.…”

Section: Pointing Techniques For Handheld Ar and Spatially-aware Intementioning

confidence: 99%

“…Rohs et al [14,15] studied pointing with a screen-centered crosshair on a phone. They showed that the performance of this technique could be modeled with a two parts Fitt's law: physical pointing (i.e.…”

Section: Pointing Techniques For Handheld Ar and Spatially-aware Intementioning

confidence: 99%

“…For handheld AR systems, pointing with a screen-centered crosshair has been studied [14,15]. This technique is impaired by viewpoint instability.…”

Section: Introductionmentioning

confidence: 99%

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Precise Pointing Techniques for Handheld Augmented Reality

Thomas

Nigay

Kurata

2013

Lecture Notes in Computer Science

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Abstract. We propose two techniques that improve accuracy of pointing at physical objects for handheld Augmented Reality (AR). In handheld AR, pointing accuracy is limited by both touch input and camera viewpoint instability due to hand jitter. The design of our techniques is based on the relationship between the touch input space and two visual reference frames for on-screen content, namely the screen and the physical object that one is pointing at. The first technique is based on Shift, a touch-based pointing technique, and video freeze, in order to combine the two reference frames for precise pointing. Contrastingly -without freezing the video-, the second technique offers a precise mode with a cursor that is stabilized on the physical object and controlled with relative touch inputs on the screen. Our experimental results show that our techniques are more accurate than the baseline techniques, namely direct touch on the video and screen-centered crosshair pointing.

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Section: Pointing Techniques For Handheld Ar and Spatially-aware Intementioning

confidence: 99%

Section: Pointing Techniques For Handheld Ar and Spatially-aware Intementioning

confidence: 99%

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Precise Pointing Techniques for Handheld Augmented Reality

Thomas

Nigay

Kurata

2013

Lecture Notes in Computer Science

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“…The technique is inspired by handheld AR systems that use a crosshair in the centre of the screen to select predefined objects (e.g. [22]). By touching the display at any position the user defines the first corner (e.g.…”

Section: Interaction Techniques To Select Regionsmentioning

confidence: 99%

Who’s That Girl? Handheld Augmented Reality for Printed Photo Books

Henze

Böll

2011

Human-Computer Interaction – INTERACT 2011

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Abstract. Augmented reality on mobile phones has recently made major progress. Lightweight, markerless object recognition and tracking makes handheld Augmented Reality feasible for new application domains. As this field is technology driven the interface design has mostly been neglected. In this paper we investigate visualization techniques for augmenting printed documents using handheld Augmented Reality. We selected the augmentation of printed photo books as our application domain because photo books are enduring artefacts that often have online galleries containing further information as digital counterpart. Based on an initial study, we designed two augmentations and three techniques to select regions in photos. In an experiment, we compare an augmentation that is aligned to the phone's display with an augmentation aligned to the physical object. We conclude that an object aligned presentation is more usable. For selecting regions we show that participants are more satisfied using simple touch input compared to Augmented Reality based input techniques.

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“…Similar to other Augmented Reality (AR) applications, a typical approach for Augmented Paper consists of three major steps: 1) identifying a paper document using its camera-captured images, 2) building precise coordinates transforms between the camera, display and document coordinates, and 3) overlaying the associated digital information on the paper document via a seethrough Magic Lens [12,13,23] or projector [11,14]. The first step, document identification, is a key step for AP.…”

Section: Introductionmentioning

confidence: 99%

Minimum correspondence sets for improving large-scale augmented paper

Yang

Liao

Liu

et al. 2011

Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry

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Augmented Paper (AP) is an important area of Augmented Reality (AR). Many AP systems rely on visual features for paper document identification. Although promising, these systems can hardly support large sets of documents (i.e. one million documents) because of the high memory and time cost in handling highdimensional features. On the other hand, general large-scale image identification techniques are not well customized to AP, costing unnecessarily more resource to achieve the identification accuracy required by AP.To address this mismatching between AP and image identification techniques, we propose a novel large-scale image identification technique well geared to AP. At its core is a geometric verification scheme based on Minimum visual-word Correspondence Set (MICSs). MICS is a set of visual word (i.e. quantized visual feature) correspondences, each of which contains a minimum number of correspondences that are sufficient for deriving a transformation hypothesis between a captured document image and an indexed image. Our method selects appropriate MICSs to vote in a Hough space of transformation parameters, and uses a robust dense region detection algorithm to locate the possible transformation models in the space. The models are then utilized to verify all the visual word correspondence to precisely identify the matching indexed image.By taking advantage of unique geometric constraints in AP, our method can significantly reduce the time and memory cost while achieving high accuracy. As showed in evaluation with two AP systems called FACT and EMM, over a dataset with 1M+ images, our method achieves 100% identification accuracy and 0.67% registration error for FACT; For EMM, our method outperforms the state-of-the-art image identification approach by achieving 4% improvements in detection rate and almost perfect precision, while saving 40% and 70% memory and time cost.

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Target acquisition with camera phones when used as magic lenses

Cited by 58 publications

References 22 publications

Precise Pointing Techniques for Handheld Augmented Reality

Precise Pointing Techniques for Handheld Augmented Reality

Who’s That Girl? Handheld Augmented Reality for Printed Photo Books

Minimum correspondence sets for improving large-scale augmented paper

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