Publication: Using crowdsourcing for scientific analysis of industrial tomographic images
Program
KU-Authors
KU Authors
Co-Authors
Chen, Chen
Wozniak, Pawel W.
Romanowski, Andrzej
Jaworski, Tomasz
Kucharski, Jacek
Grudzien, Krzysztof
Zhao, Shengdong
Fjeld, Morten
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Abstract
In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.
Source
Publisher
Association for Computing Machinery (ACM)
Subject
Computer science, Artificial intelligence, Computer science, Information systems
Citation
Has Part
Source
Acm Transactions on Intelligent Systems and Technology
Book Series Title
Edition
DOI
10.1145/2897370