当前位置: X-MOL 学术J. Real-Time Image Proc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Using augmented reality and deep learning to enhance Taxila Museum experience
Journal of Real-Time Image Processing ( IF 2.9 ) Pub Date : 2020-10-28 , DOI: 10.1007/s11554-020-01038-y
Mudassar Ali Khan , Sabahat Israr , Abeer S Almogren , Ikram Ud Din , Ahmad Almogren , Joel J. P. C. Rodrigues

Museums have adapted their traditional ways of providing services with the advent of novel digital technologies to match up with the pace and growing needs of current industry revolution. Mixed Reality has revitalized interpretation of numerous domains by offering immersive experiences in digital and real world. In the proposed study, an attempt was made to enrich user’s museum experience with relevant multimedia information and for building a better connection with the artifacts with in Taxila Museum in Pakistan, which has beautifully preserved the Gandhara civilization. The proposed solution is an Augmented Reality (AR)-based smartphone application which recognizes artifacts using Deep Learning in real time and retrieve supportive multimedia information for the visitors. To provide user with exact content, convolutional neural networks (CNN) will be applied to correctly recognize artifacts. The significance of proposed application is compared with traditional human guided or free user tours through user-centric questionnaire-based survey. The evaluation is carefully performed using relevant evaluation models including Museum Experience Scale (MES) and triptych model of interactivity. The findings of the study are discussed and assessed comprehensively using statistical methods to highlight its significance.



中文翻译:

使用增强现实和深度学习来增强塔西拉博物馆的体验

随着新的数字技术的出现,博物馆已经适应了传统的提供服务方式,以适应当前行业革命的步伐和不断增长的需求。混合现实通过在数字和现实世界中提供沉浸式体验,重振了对众多领域的诠释。在拟议的研究中,试图利用相关的多媒体信息来丰富用户的博物馆体验,并与巴基斯坦的塔西拉博物馆(Taxila Museum)建立更好的文物联系,该博物馆精美地保存了civilization陀罗文明。提出的解决方案是基于增强现实(AR)的智能手机应用程序,该应用程序使用深度学习实时识别工件并为访问者检索支持的多媒体信息。为了向用户提供准确的内容,卷积神经网络(CNN)将用于正确识别伪影。通过以用户为中心的基于问卷的调查,将建议的应用程序的重要性与传统的人类指导或免费用户访问进行了比较。使用相关的评估模型,包括博物馆体验量表(MES)和互动性的三联画模型,认真地进行了评估。使用统计方法对研究结果进行了讨论和评估,以突出其意义。

更新日期:2020-10-30
down
wechat
bug