A novel augmented reality framework for enriching museum exhibits

用于丰富博物馆展品的新颖的增强现实框架

基本信息

  • 批准号:
    507333-2016
  • 负责人:
  • 金额:
    $ 8.78万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Collaborative Research and Development Grants
  • 财政年份:
    2018
  • 资助国家:
    加拿大
  • 起止时间:
    2018-01-01 至 2019-12-31
  • 项目状态:
    已结题

项目摘要

Augmented Reality (AR) is one of the hottest emerging technologies, where the combination of virtual and real worlds can result in an immersive and natural experience for the end-user. AR has found its application in fields such as medical imaging, security, enterntainment, fitness, etc. ****Collaborating with SimentIT, Inc., we will develop new technologies that will disrupt the way people experience museum exhibits or the like of. Specifically, we will investigate : 1) 3D object recognition, where fast and accurate recognition of 3D objects (e.g. objects on display at museums) on mobile devices will result in quick instantiation of an AR experience; 2) tracking, where we will investigate novel techniques to improve the state-of-the-art in Simultaneous Localization and Mapping (SLAM) through incorporation of semantic understanding of the visual data and multimodal fusion of data obtained from on-board mobile inertial sensors; and 3) cloud processing; where intelligent client-server work distribution and highly parallel processing will further improve the processing capabilities of an AR application. ****Together, the proposed methods will result in a robust framework for creating mobile AR applications. The proposed research will help to position Canada as a leader in multimedia technologies in the 21st century. The resultant technology transfer to Canadian industry will strengthen Canada's global competitiveness and create positive impacts to Canadian economy and society.
增强现实 (AR) 是最热门的新兴技术之一,虚拟世界和现实世界的结合可以为最终用户带来身临其境的自然体验。 AR 已在医学成像、安全、娱乐、健身等领域得到应用。****与 SimentIT, Inc. 合作,我们将开发新技术,颠覆人们体验博物馆展品等的方式。 具体来说,我们将研究:1)3D 对象识别,在移动设备上快速准确地识别 3D 对象(例如博物馆展出的对象)将导致 AR 体验的快速实例化; 2) 跟踪,我们将研究新技术,通过结合视觉数据的语义理解和从机载移动惯性传感器获得的数据的多模态融合来提高同步定位和地图绘制(SLAM)的最先进水平; 3)云处理;其中智能客户端-服务器工作分配和高度并行处理将进一步提高 AR 应用程序的处理能力。 ****所提出的方法将共同形成一个用于创建移动 AR 应用程序的强大框架。拟议的研究将有助于使加拿大成为 21 世纪多媒体技术的领导者。由此产生的向加拿大工业的技术转让将增强加拿大的全球竞争力,并对加拿大经济和社会产生积极影响。

项目成果

期刊论文数量(0)
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Khan, Naimul其他文献

CNN-Based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors
  • DOI:
    10.1109/jsen.2020.3028561
  • 发表时间:
    2021-02-01
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Ahmad, Zeeshan;Khan, Naimul
  • 通讯作者:
    Khan, Naimul
Mobile Health-Supported Virtual Reality and Group Problem Management Plus: Protocol for a Cluster Randomized Trial Among Urban Refugee and Displaced Youth in Kampala, Uganda (Tushirikiane4MH, Supporting Each Other for Mental Health).
  • DOI:
    10.2196/42342
  • 发表时间:
    2022-12-08
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Logie, Carmen H;Okumu, Moses;Kortenaar, Jean-Luc;Gittings, Lesley;Khan, Naimul;Hakiza, Robert;Kibuuka Musoke, Daniel;Nakitende, Aidah;Katisi, Brenda;Kyambadde, Peter;Khan, Torsum;Lester, Richard;Mbuagbaw, Lawrence
  • 通讯作者:
    Mbuagbaw, Lawrence
Classification of lung pathologies in neonates using dual-tree complex wavelet transform.
  • DOI:
    10.1186/s12938-023-01184-x
  • 发表时间:
    2023-12-04
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Aujla, Sagarjit;Mohamed, Adel;Tan, Ryan;Magtibay, Karl;Tan, Randy;Gao, Lei;Khan, Naimul;Umapathy, Karthikeyan
  • 通讯作者:
    Umapathy, Karthikeyan
Inertial Sensor Data to Image Encoding for Human Action Recognition
  • DOI:
    10.1109/jsen.2021.3062261
  • 发表时间:
    2021-05-01
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Ahmad, Zeeshan;Khan, Naimul
  • 通讯作者:
    Khan, Naimul
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
  • DOI:
    10.1109/jsen.2019.2947446
  • 发表时间:
    2020-02-01
  • 期刊:
  • 影响因子:
    4.3
  • 作者:
    Ahmad, Zeeshan;Khan, Naimul
  • 通讯作者:
    Khan, Naimul

Khan, Naimul的其他文献

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{{ truncateString('Khan, Naimul', 18)}}的其他基金

Multimodal, Interpretable, and Interactive Machine Learning for Multimedia
多媒体的多模式、可解释和交互式机器学习
  • 批准号:
    RGPIN-2020-05471
  • 财政年份:
    2022
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Discovery Grants Program - Individual
A cloud-based Machine Learning Framework for Assessment of Stress/Engagement through Multimodal Sensors
基于云的机器学习框架,用于通过多模态传感器评估压力/参与度
  • 批准号:
    537987-2018
  • 财政年份:
    2021
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Collaborative Research and Development Grants
Multimodal, Interpretable, and Interactive Machine Learning for Multimedia
多媒体的多模式、可解释和交互式机器学习
  • 批准号:
    RGPIN-2020-05471
  • 财政年份:
    2021
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Discovery Grants Program - Individual
Multimodal, Interpretable, and Interactive Machine Learning for Multimedia
多媒体的多模式、可解释和交互式机器学习
  • 批准号:
    DGECR-2020-00438
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Discovery Launch Supplement
Multimodal, Interpretable, and Interactive Machine Learning for Multimedia
多媒体的多模式、可解释和交互式机器学习
  • 批准号:
    RGPIN-2020-05471
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Discovery Grants Program - Individual
Research and development of a cloud-based context-aware API for semantic scene understanding
基于云的上下文感知API的语义场景理解研究与开发
  • 批准号:
    558247-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Alliance Grants
COVID-19 and the Efficacy of Using Virtual Reality Scenarios to Safely Train Police in Mental Health Crisis Response
COVID-19 以及使用虚拟现实场景安全培训警察应对心理健康危机的功效
  • 批准号:
    554476-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Alliance Grants
A cloud-based Machine Learning Framework for Assessment of Stress/Engagement through Multimodal Sensors
基于云的机器学习框架,用于通过多模态传感器评估压力/参与度
  • 批准号:
    537987-2018
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Collaborative Research and Development Grants
COVID-19 - An intelligent system for contact tracing, monitoring, and privacy preserving data analytics during the COVID-19 pandemic
COVID-19 - 用于在 COVID-19 大流行期间进行接触者追踪、监控和隐私保护数据分析的智能系统
  • 批准号:
    551077-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Alliance Grants
A cloud-based Machine Learning Framework for Assessment of Stress/Engagement through Multimodal Sensors
基于云的机器学习框架,用于通过多模态传感器评估压力/参与度
  • 批准号:
    537987-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 8.78万
  • 项目类别:
    Collaborative Research and Development Grants

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