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Evaluating Pre-Processing Pipelines for Thermal-Visual Smart Camera
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion. HIAB AB. (STC)ORCID-id: 0000-0003-1923-3843
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion.ORCID-id: 0000-0002-3429-273X
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion. (STC)
2017 (engelsk)Inngår i: Proceedings of the 11th International Conference on Distributed Smart Cameras, ACM Digital Library, 2017, Vol. F132201, s. 95-100Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
ACM Digital Library, 2017. Vol. F132201, s. 95-100
Emneord [en]
Thermal imaging, FPGA, intelligence partitioning
HSV kategori
Identifikatorer
URN: urn:nbn:se:miun:diva-32437DOI: 10.1145/3131885.3131908Scopus ID: 2-s2.0-85038877488ISBN: 978-1-4503-5487-5 (tryckt)OAI: oai:DiVA.org:miun-32437DiVA, id: diva2:1165559
Konferanse
11th International Conference on Distributed Smart Cameras, Stanford University, Stanford; United States; 5 September 2017 through 7 September 2017
Prosjekter
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2017-12-13 Laget: 2017-12-13 Sist oppdatert: 2019-09-09bibliografisk kontrollert

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Shallari, IridaImran, MuhammadLawal, NajeemO'Nils, Mattias

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