Approximating the Euclidean circle in the square grid using neighbourhood sequences

Distance measuring is a very important task in digital geometry and digital image processing. Due to our natural approach to geometry we think of the set of points that are equally far from a given point as a Euclidean circle. Using the classical neighbourhood relations on digital grids, we get circles that greatly differ from the Euclidean circle. In this paper we examine different methods of approximating the Euclidean circle in the square grid, considering the possible motivat ions as well. We compare the perimeter-, area-, curve- and noncompactness-based approximations and examine their realization using neighbourhood sequences. We also provide a table which summarizes our results, and can be used when developing applications that support neighbourhood sequences. MSC2000 code: 52C99.