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Improved imaging and image analysis system for application to measurement of small ice crystals
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
Department of Earth and Environmental Sciences, University of Waterloo.
Department of Earth and Environmental Sciences, University of Waterloo.
2012 (English)In: Journal of Atmospheric and Oceanic Technology, ISSN 0739-0572, E-ISSN 1520-0426, Vol. 29, no 12, 1811-1824 p.Article in journal (Refereed) Published
Abstract [en]

Accurate knowledge of ice particle size and shape distribution is required for understanding of atmospheric microphysical processes. While larger ice particles are easily measured with a variety of sensors, the measurement of small ice particles with sizes down to a few micrometers remains challenging. Here the authors report the development of a system that measures the size and shape of small ice particles using a novel combination of high-resolution imaging and high-speed automated image classification. The optical system has a pixel resolution of 0.2 μm and a resolving power of approximately 1 μm. This imaging instrument is integrated into a cryogenic flow tube that allows precise control of experimental conditions.This study also describes an automated method for the high-speed analysis of high-resolution particle images. Each particle is located in the image using a Sobel edge detector, the border is vectorized, and a polygon representing the border is found. The vertices of this polygon are expressed in complex coordinates, and an analytic implementation of Fourier shape descriptors is used for piecewise integration along the edges of the polygon.The authors demonstrate the capabilities of this system in a study of the early-stage growth of ice particles, which are grown for approximately 1 min at fixed temperature and saturated water vapor concentrations in the cryogenic flowtube. Ice particle shapes and size distributions are reported and compared with habit diagrams found in the literature. The capability of the shape recognition system is verified by comparison with manual classification.

Place, publisher, year, edition, pages
2012. Vol. 29, no 12, 1811-1824 p.
Research subject
Space Technology
URN: urn:nbn:se:ltu:diva-2464DOI: 10.1175/JTECH-D-11-00199.1Local ID: 016bde6c-1183-4c9f-b7e2-c23ad54a4722OAI: diva2:975316
Validerad; 2013; 20130109 (thokuh)Available from: 2016-09-29 Created: 2016-09-29Bibliographically approved

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