Image-based spectroscopy for environmental monitoring

Eduard Bachmakov; Carolyn Molina; Rosalind Wynne

doi:10.1117/12.2044598

8 March 2014 Image-based spectroscopy for environmental monitoring

Eduard Bachmakov, Carolyn Molina, Rosalind Wynne

Proceedings Volume 9062, Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2014; 90620B (2014) https://doi.org/10.1117/12.2044598
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2014, San Diego, California, United States

Abstract

An image-processing algorithm for use with a nano-featured spectrometer chemical agent detection configuration is presented. The spectrometer chip acquired from Nano-Optic Devices^TM can reduce the size of the spectrometer down to a coin. The nanospectrometer chip was aligned with a 635nm laser source, objective lenses, and a CCD camera. The images from a nanospectrometer chip were collected and compared to reference spectra. Random background noise contributions were isolated and removed from the diffraction pattern image analysis via a threshold filter. Results are provided for the image-based detection of the diffraction pattern produced by the nanospectrometer. The featured PCF spectrometer has the potential to measure optical absorption spectra in order to detect trace amounts of contaminants. MATLAB tools allow for implementation of intelligent, automatic detection of the relevant sub-patterns in the diffraction patterns and subsequent extraction of the parameters using region-detection algorithms such as the generalized Hough transform, which detects specific shapes within the image. This transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. By employing this imageprocessing technique, future sensor systems will benefit from new applications such as unsupervised environmental monitoring of air or water quality.

Citation Download Citation

Eduard Bachmakov, Carolyn Molina, and Rosalind Wynne "Image-based spectroscopy for environmental monitoring", Proc. SPIE 9062, Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2014, 90620B (8 March 2014); https://doi.org/10.1117/12.2044598

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KEYWORDS

Diffraction

Hough transforms

Spectroscopy

MATLAB

CCD cameras

Edge detection

Environmental monitoring

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