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Evaluating Pre-Processing Pipelines for Thermal-Visual Smart Camera
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. HIAB AB. (STC)ORCID iD: 0000-0003-1923-3843
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-3429-273X
2017 (English)In: Proceedings of the 11th International Conference on Distributed Smart Cameras, ACM Digital Library, 2017, Vol. F132201, p. 95-100Conference paper, Published paper (Refereed)
Abstract [en]

Smart camera systems integrating multi-model image sensors provide better spectral sensitivity and hence better pass-fail decisions. In a given vision system, pre-processing tasks have a ripple effect on output data and pass-fail decision of high level tasks such as feature extraction, classification and recognition. In this work, we investigated four pre-processing pipelines and evaluated the effect on classification accuracy and output transmission data. The pre-processing pipelines processed four types of images, thermal grayscale, thermal binary, visual and visual binary. The results show that the pre-processing pipeline, which transmits visual compressed Region of Interest (ROI) images, offers 13 to 64 percent better classification accuracy as compared to thermal grayscale, thermal binary and visual binary. The results show that visual raw and visual compressed ROI with suitable quantization matrix offers similar classification accuracy but visual compressed ROI offers up to 99 percent reduced communication data as compared to visual ROI.

Place, publisher, year, edition, pages
ACM Digital Library, 2017. Vol. F132201, p. 95-100
Keywords [en]
Thermal imaging, FPGA, intelligence partitioning
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-32437DOI: 10.1145/3131885.3131908Scopus ID: 2-s2.0-85038877488ISBN: 978-1-4503-5487-5 (print)OAI: oai:DiVA.org:miun-32437DiVA, id: diva2:1165559
Conference
11th International Conference on Distributed Smart Cameras, Stanford University, Stanford; United States; 5 September 2017 through 7 September 2017
Projects
SMART
Funder
Knowledge FoundationAvailable from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-01-30Bibliographically approved

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Shallari, IridaImran, MuhammadO'Nils, MattiasLawal, Najeem

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CiteExportLink to record
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