Ubiquitous Metadata: Design and Fabrication of Embedded Markers for Real-World Object Identification and Interaction
Mustafa Doga Dogan

TL;DR
This paper introduces a comprehensive framework for designing, fabricating, and detecting embedded machine-readable markers in physical objects to enable seamless real-world and digital interactions across various domains.
Contribution
It proposes novel natural, structural, and internal marker approaches that integrate computer vision, machine learning, and material science for robust object identification.
Findings
Effective object identification and tracking demonstrated
Markers enable diverse applications in multiple domains
Robust detection methods for embedded markers developed
Abstract
The convergence of the physical and digital realms has ushered in a new era of immersive experiences and seamless interactions. As the boundaries between the real world and virtual environments blur and result in a "mixed reality," there arises a need for robust and efficient methods to connect physical objects with their virtual counterparts. In this thesis, we present a novel approach to bridging this gap through the design, fabrication, and detection of embedded machine-readable markers. We categorize the proposed marking approaches into three distinct categories: natural markers, structural markers, and internal markers. Natural markers, such as those used in SensiCut, are inherent fingerprints of objects repurposed as machine-readable identifiers, while structural markers, such as StructCode and G-ID, leverage the structural artifacts in objects that emerge during the fabrication…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMobile and Web Applications · Context-Aware Activity Recognition Systems · Web Data Mining and Analysis
