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Simultaneous sensing, readout, and classification on an intensity-ranking image sensor
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6763-5487
Swedish Natl Forens Ctr NFC, Linkoping, Sweden.
Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, Faculty of Science & Engineering.
2018 (English)In: International journal of circuit theory and applications, ISSN 0098-9886, E-ISSN 1097-007X, Vol. 46, no 9, p. 1606-1619Article in journal (Refereed) Published
Abstract [en]

We combine the near-sensor image processing concept with address-event representation leading to an intensity-ranking image sensor (IRIS) and show the benefits of using this type of sensor for image classification. The functionality of IRIS is to output pixel coordinates (X and Y values) continuously as each pixel has collected a certain number of photons. Thus, the pixel outputs will be automatically intensity ranked. By keeping track of the timing of these events, it is possible to record the full dynamic range of the image. However, in many cases, this is not necessary-the intensity ranking in itself gives the needed information for the task at hand. This paper describes techniques for classification and proposes a particular variant (groves) that fits the IRIS architecture well as it can work on the intensity rankings only. Simulation results using the CIFAR-10 dataset compare the results of the proposed method with the more conventional ferns technique. It is concluded that the simultaneous sensing and classification obtainable with the IRIS sensor yields both fast (shorter than full exposure time) and processing-efficient classification.

Place, publisher, year, edition, pages
WILEY , 2018. Vol. 46, no 9, p. 1606-1619
Keywords [en]
image sensors; image classification; machine learning; near-sensor processing
National Category
Condensed Matter Physics
Identifiers
URN: urn:nbn:se:liu:diva-151785DOI: 10.1002/cta.2549ISI: 000445184900004OAI: oai:DiVA.org:liu-151785DiVA, id: diva2:1254020
Note

Funding Agencies|Swedish Research Council (Vetenskapsradet) [2014-6227]

Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2018-11-07

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