  The instance segmentation can be considered an extension of the object
detection problem where bounding boxes are replaced by object contours.
Strictly speaking the problem requires to identify each pixel instance and
class independently of the artifice used for this mean. The advantage of
instance segmentation over the usual object detection lies in the precise
delineation of objects improving object localization. Additionally, object
contours allow the evaluation of partial occlusion with basic image processing
algorithms. This work approaches the instance segmentation problem as an
annotation problem and presents a novel technique to encode and decode ground
truth annotations. We propose a mathematical representation of instances that
any deep semantic segmentation model can learn and generalize. Each individual
instance is represented by a center of mass and a field of vectors pointing to
it. This encoding technique has been denominated Distance to Center of Mass
Encoding (DCME).
