A Parallel, O(n) Algorithm for an Unbiased, Thin Watershed

International audienceThe watershed transform is a powerful tool for morphological segmentation. Most common implementations of this method involve a strict hierarchy on gray tones in processing the pixels composing an image. This hierarchical dependency complexifies the efficient use of modern computational architectures. This paper introduces a new way of computing the watershed transform that alleviates the sequential nature of hierarchical queue propagation. It is shown that this method can directly relate to the hierarchical flooding. Simultaneous and disorderly growth can now be used to maximize performances on modern architectures. Higher speed is reached, bigger data volume can be processed. Experimental results show increased performances regarding execution speed and memory consumption