Object counting application specifications

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  • Count compact homogeneous objects on optical high resolution images
    • Examples of objects: buildings, isolated trees, vehicles, etc.
  • Inputs for the algorithm
    • The image to process
    • A set of ROIs selected by an operator on examples of objects to count
  • Outputs of the algorithm
    • The number of objects found
    • A list of polygons which fit the detected objects
  • Algorithm description
    • A binary image resulting from a supervised classification is generated from the input image and the training ROIs
      • A simplified version of this can be a spectral angle with respect to the mean of all ROIs (or the min SAM to the mean of each ROI) and an automatic threshold (otsu)
    • The input image is clustered using a MeanShift filter. The boundaries are also generated from the MeanShift.
      • The classified image and the mean shift results are fused in order to generate a set of disconnected regions belonging to the detected class
      • The edges of the regions are converted to polygons
      • The polygons can be refined in order to better fit the objects

Use case

    • Open an optical HR image
    • Select examples of objects to count
    • Trigger the counting
    • Display on a text field the resulting number of objects found
    • Overlay polygons on top of the detected objects
      • Put a text label on the polygon with the object id


    • A raster image with a unique label per object (unsigned short)
    • A vector data file with one polygon per object

States of the application and transitions