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Related Concept Videos

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis
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Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth

Samuli Rahkonen1, Leevi Lind1, Anna-Maria Raita-Hakola1

  • 1Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland.

Sensors (Basel, Switzerland)
|November 26, 2022
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Summary

This study presents a novel method for fusing hyperspectral imaging and depth sensing data. The developed technique accurately combines 3D point cloud and hyperspectral information, enabling precise reflectance measurements.

Keywords:
depth datahyperspectralkinectreflectancesensor fusion

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Area of Science:

  • Multispectral and Hyperspectral Imaging
  • Computer Vision
  • Optical Sensing

Background:

  • Sensor fusion of hyperspectral and depth data is challenging due to optical properties and calibration requirements.
  • Existing applications span aerial, forestry, agricultural, and medical imaging.
  • Combining diverse imaging modalities requires precise sensor calibration and registration.

Purpose of the Study:

  • To demonstrate a method for fusing data from a Fabry-Perot interferometer hyperspectral camera and a Kinect V2 depth sensing camera.
  • To utilize depth-augmented hyperspectral data for measuring emission angle-dependent reflectance.
  • To validate the fusion method using a multi-view inferred point cloud.

Main Methods:

  • Calibrated intrinsic and extrinsic camera parameters for both sensors.
  • Employed global and local registration algorithms to merge point clouds from multiple viewpoints.
  • Generated a dense point cloud and calculated angle-dependent reflectances.
  • Validated the method on a reference colorchecker board.

Main Results:

  • Successfully combined 3D point cloud and hyperspectral data from different viewpoints.
  • Achieved reliable point cloud registration with 0.29-0.36 fitness and RMSE of approximately 2.
  • Measured reflectance RMSE between 0.01-0.05 and spectral angle between 1.5-3.2 degrees.
  • Demonstrated minimal effect of emission angle on surface reflectance intensity and spectrum.

Conclusions:

  • The developed method effectively fuses hyperspectral and depth data for accurate 3D scene reconstruction and spectral analysis.
  • The fusion technique provides reliable measurements of angle-dependent reflectance.
  • The findings confirm the limited impact of emission angle on the reflectance properties of the tested material.