Augmented reality on smartphones

Henrysson, Anders; Ollila, Mark

doi:10.1109/art.2003.1320421

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…This trend has been supported by many studies [2,3,4,5,6,7,8]. The users of these devices demand quality that is similar to that of a professional camera.…”

Section: Introductionmentioning

confidence: 78%

Design and development of de-blur/de-noising filter using multiple image captures on a massively parallel architecture

Jain¹,

Bhandari²,

Shah³

et al. 2014

2014 Annual IEEE India Conference (INDICON)

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Now a days mobile devices are capable of performing more operations in less amount of time. More number of GPU and CPU cores are added to the mobile devices. Due to increase in processing power various image processing algorithms are targeted at mobile devices. The users of these devices demand quality that is similar to that of a professional camera. There are definite limitations on the sensors that can be used in a smart phone. However, with the increase in computing power of smart phones/tablets, better software can be developed to compensate for these shortcomings. The image capture in low light condition can be enhanced by using guided filter. In this paper, we present the implementation of the Guided filter on Nexus 4. It has been observed that the speedup increases with respect to increase in size of image and size of window.

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“…This trend has been supported by many studies [2,3,4,5,6,7,8]. The users of these devices demand quality that is similar to that of a professional camera.…”

Section: Introductionmentioning

confidence: 78%

Design and development of de-blur/de-noising filter using multiple image captures on a massively parallel architecture

Jain¹,

Bhandari²,

Shah³

et al. 2014

2014 Annual IEEE India Conference (INDICON)

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“…Besides specialised AR hardware, such as Microsoft's HoloLens [19] or Google's Glass [20], augmented reality has been available on smartphones since their introduction in the early 2000s [21]. Since then, the smartphone industry has become dominated by Apple and Google, as far as operating systems go.…”

Section: Arcorementioning

confidence: 99%

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

et al. 2020

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The fast evolution in computational and sensor technologies brings previously niche solutions to a wider userbase. As such, 3D reconstruction technologies are reaching new use-cases in scientific and everyday areas where they were not present before. Cost-effective and easy-to-use solutions include camera-based 3D scanning techniques, such as photogrammetry. This paper provides an overview of the available solutions and discusses in detail the depth-image based Real-time Appearance-based Mapping (RTAB-Map) technique as well as a smartphone-based solution that utilises ARCore, the Augmented Reality (AR) framework of Google. To qualitatively compare the two 3D reconstruction technologies, a simple length measurement-based method was applied with a purpose-designed reference object. The captured data were then analysed by a processing algorithm. In addition to the experimental results, specific case studies are briefly discussed, evaluating the applicability based on the capabilities of the technologies. As such, the paper presents the use-case of interior surveying in an automated laboratory as well as an example for using the discussed techniques for landmark surveying. The major findings are that point clouds created with these technologies provide a direction- and shape-accurate model, but those contain mesh continuity errors, and the estimated scale factor has a large standard deviation.

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“…The analysis of performance of Forms P 1 and P 2 at the item level wes based on classical or 'true score" theory (Gullksen, 1950;Henrysson, 1971).…”

Section: Classical Item Analysismentioning

confidence: 99%

Air Force Officer Qualifying Test (AFOQT): Forms P Pre-Implementation Analyses and Equating

Steuck¹,

Watson²,

Skinner³

1988

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Is. REPORT SECURITY CLASSiFICATION 1b. RESTRICTIVE MARKINGSl Unclassified ___________________________ 2.. SECURITY CLASSIIFICATION AUTKORITY 3. OISTIBUTIONIAVAaIASUTY OF REPORT 2. DICAICAIONOOWNGRAENG SCHEDULE Approved for public release; distribution is unlimited. 4. PERPORMIING ORGANIZATION REPORT NUMBERS) S. MONITORING ORGANIZATION REPORT NUM&6R(S) SUMMY This paper describes the provisional equating of Form P 1 and P 2 of the Air Force Officer Qualifying Test (AFOQT) and the associated analyses in preparation for its operational implementation in 1987. The pre-imlemntation equating was necessary (a) to check the adequacy of the items in the new forms, (b) to assess the similarity of the new forms, and (c) to establish conversion tables for placing scores from the new test on the mtric of Form 0. Three forms of the AFOQT (0, P 1 , and P 2) ware adeinistered to about 3,400 military subjects at 11 Air Force bases. The subjects wre from Basic Military Training School (ONTS), Air Force Reserve Officer Training Corps (AFROTC), and Officer Training School (OTS). Analyses ware computed at the item, subtest, and composite levels and several types of equatings ware completed. The distributions of items based on item difficulty and item discrimination were similar across forms, but not Identical. Equipercentile equatings were used to produce conversion tables for Forms P for provisional use prior to the Initial Operational Test and Evaluation (IOT&E) of the new forms. PREFACE The Air Force Human Resources Laboratory (AFHRL) is tasked as the test development agency for the Air Force Officer Qualifying Test (AFOOT) by Air Force Regulation 35-8, Air Force Military Personnel Testing System. The current research and development (R&D) effort was undertaken as part of AFHRL's responsibility to develop, revise, and conduct research in support of the AFOQT. Work was accomplished under Task 771918, Selection and Classification Technologies, which is part of a larger effort In Force Acquisition and Distribution Systems. The study was completed under Work Units 77191847 (Development and Validation of Civilian and Nonrated Officer Selection Methodologies) and 77191824 (Officer Item Pool Development).

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Augmented reality on smartphones

Cited by 8 publications

References 1 publication

Design and development of de-blur/de-noising filter using multiple image captures on a massively parallel architecture

Design and development of de-blur/de-noising filter using multiple image captures on a massively parallel architecture

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

Air Force Officer Qualifying Test (AFOQT): Forms P Pre-Implementation Analyses and Equating

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