Designing a marker set for vertical tangible user interfaces

Conference paper

Attard, G., De Raffaele, C. and Smith, S. 2021. Designing a marker set for vertical tangible user interfaces. 10th ICIT 2021: Advanced Machine Learning and Deep Learning. Amman, Jordan 14 - 15 Jul 2021 IEEE. pp. 974-979 https://doi.org/10.1109/ICIT52682.2021.9491659
TypeConference paper
TitleDesigning a marker set for vertical tangible user interfaces
AuthorsAttard, G., De Raffaele, C. and Smith, S.
Abstract

Tangible User Interfaces (TUI)s extend the domain of reality-based human-computer interaction by providing users the ability to manipulate digital data using physical objects which embody representational significance. Whilst various advancements have been registered over the past years through the development and availability of TUI toolkits, these have mostly converged towards the deployment of tabletop TUI architectures. In this context, markers used in current toolkits can only be placed underneath the tangible objects to provide recognition. Albeit being effective in various literature studies, the limitations and challenges of deploying tabletop architectures have significantly hindered the proliferation of TUI technology due to the limited audience reach such systems can provide. Furthermore, available marker sets restrict the placement and use of tangible objects since if placed on top of the tangible object, the marker will interfere with the shape and texture of the object limiting the effect the TUI has on the end-user. To this end, this paper proposes the design and development of an innovative tangible marker set specifically designed towards the development of vertical TUIs. The proposed marker set design was optimized through a genetic algorithms to ensure robustness in scale invariance, the capability of being successfully detected with distances of up to 3.5 meters and a true occlusion resistance of up to 25%, where the marker is recognized and not tracked. Open-source versions of the marker set are provided through research license on www.geoffslab.com/tangiboard_marker_set.

LanguageEnglish
Conference10th ICIT 2021: Advanced Machine Learning and Deep Learning
Page range974-979
ISBN
Electronic9781665428705
Paperback9781665428712
PublisherIEEE
Publication dates
Online26 Jul 2021
Print14 Jul 2021
Publication process dates
Deposited08 Nov 2021
Accepted12 May 2021
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Digital Object Identifier (DOI)https://doi.org/10.1109/ICIT52682.2021.9491659
Book title2021 International Conference on Information Technology (ICIT)
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