Adaptive projection augmented reality with object recognition based on deep learning

Park, Yoon Jung; Ro, Hyocheol; Byun, Jung-Hyun; Han, Tack-Don

doi:10.1145/3308557.3308678

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…An adaptive projection augmented reality (AR) system that uses deep learning-based object recognition to project virtual data onto real objects, providing rich visual feedback, information and user interfaces to users was also presented by Park et al. This innovative approach not only enhances the user experience, but also addresses the problem of high installation costs typically associated with traditional projection AR systems and offers a more cost-effective alternative [212].…”

Section: ) Adaptive Deep Learning Enabled Context-aware Interactionmentioning

confidence: 99%

Toward Trustworthy Metaverse: Advancements and Challenges

Jim,

Hosain,

Mridha

et al. 2023

IEEE Access

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The Metaverse, a transformative digital realm, holds immense promise for reshaping industries and human interactions while potentially addressing global challenges and democratizing opportunities. However, it also introduces a spectrum of complexities that demand careful navigation. To establish trustworthiness within the Metaverse ecosystem, gaining a deep understanding of its applications, challenges, and existing solutions is imperative. In this comprehensive survey, we first delve into Metaverse applications, drawing insights from existing literature. Subsequently, we explore the diverse challenges the Metaverse presents, analyzing them through the lens of existing research. We then scrutinize the overall trustworthiness of the Metaverse environment and investigate existing solutions to previously identified challenges through a thorough review and analysis of pertinent literature. Lastly, we discussed future research directions aimed at fostering a trustworthy Metaverse environment. This comprehensive review can provide an overview of the Metaverse, its application domains, challenges, existing solutions and research directions for many multidisciplinary studies.

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Section: ) Adaptive Deep Learning Enabled Context-aware Interactionmentioning

confidence: 99%

Toward Trustworthy Metaverse: Advancements and Challenges

Jim,

Hosain,

Mridha

et al. 2023

IEEE Access

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“…The hardware components were then used to define stereotypes (column 2) from which Device metaclass (column 1) was derived. Depth Sensor [7, 10, 16, 29, 31, 38-40, 42, 46] Infrared Sensor [27,36] Self-built Sensor [24] Imaging Device Projector [7,11,15,16,27,29,31,32,36,37,[39][40][41][42]46] Display [11,41] Actuator Device Actuator [15,27,32,36,40,42] Human Interface Device Mobile Device [16,24,29,31,32,37,40,42] Peripheral Device Phicon [36] Marker [24,27,32,47] Infrared lamp [27,36,37] Mirror [11,27,36] Audio Device Transducer [11]…”

Section: Hardware Environmentmentioning

confidence: 99%

“…However, the accelerated development of these technologies discontinues the usability of toolkits, in some cases making toolkits obsolete. This setback is also evident in recent research reports that suggest the adoption of existing toolkits is marginalized during the development of prototypes [11,31]. One approach to address this challenge is establishing future-proof hardware and software interfaces based on the study of prevailing interactive surface environments.…”

Section: Introductionmentioning

confidence: 99%

Model-driven development for interactive surface prototypes

Mthunzi¹,

Echtler²

2021

Preprint

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In the research space of interactive surface environments, toolkits have a central role in rapid prototyping. They simplify operating both hardware and software technologies. However, the accelerated development of these technologies discontinues the usability of toolkits, in some cases making toolkits obsolete. One approach to address this challenge is establishing future-proof hardware and software interfaces based on the study of prevailing interactive surface environments. In this paper, we study interactive surface implementations and proposes a metamodeling infrastructure to support the analysis of prevailing implementations toward designing future-proof hardware and software modeling constructs. Our approach employs the unified modeling language and emphasizes the flexible description of existing systems. To evaluate the proposed approach, six existing research prototypes have been used to conduct traces, and the consistency demonstrated is promising. A face validation study with experts has also been conducted. Expert perceptions from the face validation study suggest potential benefit in using the UML-based approach as a shared notation for studying interactive surface environments.

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“…Commodity depth cameras have promoted using projector-camera systems to augment un-instrumented surfaces such as tabletops [5], floors [18], and walls [8]. One prominent strategy is combining image processing and depth perception-based techniques [9,15,17,19]. While 2D techniques enable detecting interactions [2-4, 10, 16], solely relying on 2D methods limits the solution space of interactive surface applications.…”

Section: Introductionmentioning

confidence: 99%