The Vectorial Minimum Barrier Distance

We introduce the vectorial Minimum Barrier Distance (MBD), a method for computing a gray-weighted distance transform while also incorporating information from vectorial data. Compared to other similar tools that use vectorial data, the proposed method requires no training and does not assume having only one background class. We describe a region-growing algorithm for computing the vectorial MBD efficiently. The method is evaluated on two types of multi-channel images: color images and textural features. Different path-cost functions for calculating the multidimensional path-cost distance are also compared. The results show that by combining multi-channel images into vectorial information the performance of the vectorial MBD segmentation is improved compared to when one channel is used. This implies that the method can be a good way of incorporating multi-channel information in interactive segmentation.