Research Papers

Algorithm for particle detection and parameterization in high-frame-rate thermal video

[+] Author Affiliations
Maxime Bombrun

Clermont-Université, Université Blaise Pascal, LIMOS, BP 10448, F-63000 Clermont-Ferrand, France

CNRS, UMR 6158, LIMOS, F-63173 Aubiere, France

Clermont-Université, Université Blaise Pascal, LMV, BP 10448, F-63000 Clermont-Ferrand, France

CNRS, UMR 6524, LMV, F-63173 Aubiere, France

Vincent Barra

Clermont-Université, Université Blaise Pascal, LIMOS, BP 10448, F-63000 Clermont-Ferrand, France

CNRS, UMR 6158, LIMOS, F-63173 Aubiere, France

Andrew Harris

Clermont-Université, Université Blaise Pascal, LMV, BP 10448, F-63000 Clermont-Ferrand, France

CNRS, UMR 6524, LMV, F-63173 Aubiere, France

J. Appl. Remote Sens. 8(1), 083549 (Sep 29, 2014). doi:10.1117/1.JRS.8.083549
History: Received June 3, 2014; Revised August 29, 2014; Accepted September 3, 2014
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Abstract.  Over the last decade, feature tracking and recognition in infrared (IR) video has become an important strategy used in many applications. To achieve such a capability, we developed a method based on the top-hat transform, hybridized with refinement by thresholding. Our algorithm uses two different but correlated background subtraction approaches to clean the image. A mathematical-morphology-based method was then applied to enhance the contrast between particles and background. The algorithm was tested using images acquired during a controlled experiment and was compared with another particle tracking velocimetry method. We demonstrate that our algorithm can detect dim IR targets and enables computation of a local velocity field that can be used for the tracking step. Using this method, we were able to obtain both the distribution of particle sizes, volumes (or masses), and velocities. We also apply our algorithm to images recorded during ballistic emitting explosive events at Stromboli volcano (Italy) and favorably compare our results with other volcanologic data sets. Experimental results demonstrate that our algorithm achieves a high recognition accuracy with a low-computational cost.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

Particles ; Video ; Cameras

Citation

Maxime Bombrun ; Vincent Barra and Andrew Harris
"Algorithm for particle detection and parameterization in high-frame-rate thermal video", J. Appl. Remote Sens. 8(1), 083549 (Sep 29, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083549


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