Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine

The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.