Sequential local transform algorithms for gray-level distance transforms

In this paper, new algorithms are presented for the calculation of gray-level distance transforms, in which the distance values of pixels are proportional to the gray-value differences of minimal paths [5], not the gray values themselves, which is the case in [1] and [3]. The presented algorithms are sequential local transform algorithms. The performance of the algorithms are evaluated by testing how quickly they converge to the error-free distance image. It is shown that the presented algorithms are faster than the previously presented algorithms [5] for gray-level distance transforms. It turns out that the 4-neighbor, 4-raster algorithm is the fastest, converging to a distance image with no erroneous pixels in only two iteration rounds. Furthermore, because of the raster scanning approach they are easily applied other image grids than the rectangular one, and are easy to implement.