NDLI: Detection and classification of nano-scale particles with image histograms

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Detection and classification of nano-scale particles with image histograms

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Detection and classification of nano-scale particles with image histograms

Content Provider	ACM Digital Library
Author	Pahalawatta, Kapila K. Green, Richard
Abstract	In the present study, image histogram features of both single and multi channel images were used to detect and classify the common household/office smoke and other nano-scale airborne particles. To capture the Rayleigh scattered light from the particles, a white LED which covers most of the wavelengths of visible range was used as the light source. By considering the selective colour scattering behaviour of Rayleigh scattering, the intensity variation of red, green and blue components of a scattered light by the test particles are investigated using the image histograms. Particles were classified using feature vectors constructed using the running mean and mode of the histogram maximum value index (MVI) over a window of a fixed number of frames. Two classifiers: multiple discriminant analysis (MDA) and K-nearest neighbour algorithm (for comparison) were used to classify the feature vectors. Results showed that all the test particles can be detected and classified using a white LED, which is a significant step up over detecting smoke with traditional infrared LED light detectors. Even though, the feature vectors extracted with the proposed method from both colour and single channel image histograms classified all the monotype test particles with 100% accuracy with the both classifiers, the fastest classification was obtained with MVI of single channel images classified with MDA.
Starting Page	446
Ending Page	451
Page Count	6
File Format	PDF
ISBN	9781450314732
DOI	10.1145/2425836.2425921
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Image histogram Light scattering Particle classification Histogram maximum value index
Content Type	Text
Resource Type	Article

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