Shape priors extraction and application for geodesic distance transforms in images and videos

• We apply shape priors in geodesic distance transform segmentation.
• We integrate geometric distance and weighted gradients.
• We also consider distances to the boundary of shape priors.
• Segmentation results in line with the real shape of a particular kind of object.

We present a shape prior embedded geodesic distance transform for image and video segmentation. Whilst the existing segmentation algorithms have achieved impressive performance in many examples, they may fail in cases where the quality of the likelihood images is not satisfactory, or where multiple similar objects are in close proximity to one another. To deal with these problems, we embed shape prior knowledge in an image segmentation algorithm based on geodesic distance transform. Different from other segmentation methods, the proposed geodesic distance transform morphology operators consider three factors simultaneously: the geometric distance, weighted gradients, and the distance to the boundary of shape priors. As a result, it provides segmentation in line with the real shape of a particular kind of object. We also propose an effective shape prior extraction method that compute shape priors automatically. The proposed algorithm demonstrates positive results for many challenging images and video sequences in our experiments.