title: Stagnant zone segmentation with U-net
abstract: Silo discharging and monitoring the process for
  industrial or research application depend on computerized
  segmentation of different parts of images such as stagnant and
  flowing zones which is the toughest task. X-ray Computed
  Tomography (CT) is one of a powerful non-destructive technique
  for cross-sectional images of a 3D object based on X-ray
  absorption. CT is the most proficient for investigating different
  granular flow phenomena and segmentation of the stagnant zone
  as compared to other imaging techniques. In any case, manual
  segmentation is tiresome and erroneous for further investigations.
  Hence, automatic and precise strategies are required. In the
  present work, a U-net architecture is used for segmenting the
  stagnant zone during silo discharging process. This proposed
  image segmentation method provides fast and effective outcomes
  by exploiting a convolutional neural networks technique with an
  accuracy of 97 percent
authors: Selam Waktola Laurent Babout Krzysztof Grudzien
hIndex: Laurent Babout 20