Spectral Imaging and its Applications
Photonics Finland
PhotonHub Demo centre
English
16. 11. 2021
Free-Space Photonic Components and Systems
Mobility & energy Agro & food Safety, security, space & defence Health Other

Spectral imaging is powerful tool in modern science, machine vision, medicine, industry, agriculture, forestry, arts, and many other areas of modern human activities. Spectral imaging provides an easy way to compare and distinguish objects which look similar to human eyes, but actually different.

This one-day hands-on training course provides opportunity to understand how you can apply spectral imaging in your own field: science, industry, biology, medicine, arts.

The course will focus on three technology demonstrations:

  1. Spectra and spectroscopy – a simple way to distinguish objects;
  2. Spectral cameras and their application;
  3. Spectral data processing using non-commercial software (on example of Python).

Course attendees will learn how they could arrange spectral measurements and spectral data processing in their own field.

  • Get the idea what is a light spectrum and what is spectral data.
  • Learn how spectral cameras allow to get images where each spatial pixel is spectrum
  • Learn that processing of spectal data is very similar to processing of traditional photos and RGB images (such as png, bmp, jpg etc)
  • Understanding how spectral imaging can be applied in modern science, medicine or industry
No experience
It is desirable that course attendees have a basic understanding of photonics: light, light spectrum, infrared light, wavelengths. Understanding of programming at basic level is required: variables and arrays, for loops, if else, numerical data formats (such as integers and floats). Understanding of image processing is desirable but not essential.
Computational Spectral Imaging Lab
Joensuu
Finland
250 EUR
inncluding catering and project consumables
12 persons
#Spectroscopy#python#spectral_analysis#tunable_light_sources#infrared_imaging#ultraviolet_imaging#medical_imaging#imaging_in_forestry#industrial_imaging#big_data_analysis