Computer vision represents research into the main data modality that the professorship focusses on: image and video data. This includes the research and development in traditional computer vision algorithms and imaging: 
  • Image acquisition research focusses on how to optimally collect image data using state-of-the-art equipment. This encompasses choice of illumination, lasers, lenses, optical filters and cameras for high resolution and high-speed imaging using area scan, line scan or mosaic sensors. 

  • Hyper-spectral and multi-spectral are advanced image acquisition methods to collect image data in a broad range of visual spectra with narrow spectral bands. This has applications in detecting and recognizing materials using Short Wave Infrared (SWIR) or to measure heat sources using Long Wave Infrared (LWIR). 

  • Pattern recognition includes research into the application of traditional machine vision and machine learning algorithms where pure deep-learning solutions struggle. Applications include 3d geometry using multi-camera set-ups, key-point detection and sub-millimeter measurement. 

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