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Using Crowdsourcing...
Using Crowdsourcing for Scientific Analysis of Industrial Tomographic Images
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- Chen, C. (author)
- Universiti Kebangsaan Singapura (NUS),National University of Singapore (NUS)
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- Wozniak, Pawel, 1988 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Romanowski, A. (author)
- Politechnika Lodzka,Lodz University of Technology
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- Obaid, Mohammad, 1982 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Jaworski, T. (author)
- Politechnika Lodzka,Lodz University of Technology
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- Kucharski, J. (author)
- Politechnika Lodzka,Lodz University of Technology
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- Grudzień, K. (author)
- Politechnika Lodzka,Lodz University of Technology
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- Zhao, S. D. (author)
- Universiti Kebangsaan Singapura (NUS),National University of Singapore (NUS)
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- Fjeld, Morten, 1965 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2016-07-12
- 2016
- English.
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In: ACM Transactions on Intelligent Systems and Technology. - : Association for Computing Machinery (ACM). - 2157-6912 .- 2157-6904. ; 7:4
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
Keyword
- Design
- Algorithms
- silo
- Silo
- Computer Science
- algorithms
- crowdsourcing
- particle tracking
- hough transform
- tomography
- circle detection
- Human Factors
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Chen, C.
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Wozniak, Pawel, ...
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Romanowski, A.
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Obaid, Mohammad, ...
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Jaworski, T.
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Kucharski, J.
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show more...
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Grudzień, K.
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Zhao, S. D.
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Fjeld, Morten, 1 ...
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Computer and Inf ...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Mathematics
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and Computational Ma ...
- Articles in the publication
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ACM Transactions ...
- By the university
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Chalmers University of Technology