**1. Introduction**

The concept of the grid is essential and heavily used in digital geometry and in digital image processing. A grid is comprised of discrete points addressed with integer vectors. There are three regular tessellations of the plane, which define the square, hexagonal, and triangular grids (named after the form of the pixels used as tiles) [1]. Most of the applications use the square grid because its orthogonal coordinate system, known as the Cartesian coordinate system (CCS), which fits very well to it. This addresses each square pixel of the grid by a pair of independent integers. The dual of the square grid (the grid formed by the nodes, which are the crossing points of the gridlines) is again a square grid. Therefore, essentially, the same CCS is used as well. Working with real images, we may need to perform operations that do not map the grid to itself, e.g., zooming or rotations. The Cartesian coordinate system allows real numbers to be used in such cases. Moreover, the digitization operation can easily be defined by a rounding operation.

The hexagonal grid, tiling the plane by the same size regular hexagons (hexagonal pixels), has been used for decades in image processing applications [2], in cartography [3,4], in biological simulations [5], and in other fields, since the digital geometry of the hexagonal grid provides better results than the square grid in various cases. In addition, it is used in various table and computer games based on its compactness. It is the simplest 2D grid, since the only usual neighborhood using the nearest neighbors is simpler and less confusing than the two types of neighbors in the square grid [6]. The neighborhood of a pixel contains six other hexagons (see Figure 1a). In contrast to the square grid, the dual of the hexagonal grid is not the hexagonal, but the triangular grid (see Figure 1a,b). Adequate and elegant coordinate systems for these kinds of grids are required for their use in both theory and applications, e.g., in image processing or engineering applications. In Reference [7], a three-coordinate-valued system of zero-sum triplets is used to describe the hexagonal grid capturing the triangular symmetry of the grid. In Figure 1a, the first coordinate value is ascending right-upwardly, the second values are ascending into the right-downward direction, and the third one is ascending into the left-upward direction [7,8]. We should mention that this system could be seen as the extension of the oblique coordinate system using two independent integer values [9] by concerning the third value to obtain zero-sum for every triplet. The digital distance based on the neighborhood relation is computed in Reference [9]. Since the vectors describing the grid are not orthogonal, some geometric descriptions based on Cartesian coordinates are not very clear. However, to simplify the expressions of the constrained three-dimensional coordinate system is recommended. We should also mention that 0-sum triplets allowing real numbers were used in Reference [8] to describe rotations (that may not map the hexagonal grid to itself). In this way, a useful digitization operator is found. Her's system was mentioned and used in References [10,11] for various imaging-related disciplines.

**Figure 1.** The coordinate system for the hexagonal grid (**a**) and its dual (**b**). The coordinate system for the triangular grid (**c**) and its dual (**d**).

The triangular grid is the third regular grid. It is generated by tiling the plane regularly with equilateral triangles. Although it is the most complex among the three regular tessellations (it has the largest number and types of neighbors), it has various advantages in applications, e.g., by the flexibility of the used neighborhood. The triangular grid is built by triangles in two different orientations. Moreover, it is not a point lattice since some of the grid vectors, i.e., the vectors connecting the midpoints of triangles with different orientations, do not translate the grid into itself [12]. However, the triangular grid gives a valid alternative for applications in image processing. In some cases, it gives better results than the usual square grid due to its better symmetric properties and larger, natural neighborhood structure. Each pixel has 12 neighbors sharing at least a corner. They are categorized into three types of neighbor relations [13,14]. The triangular grid has similar symmetry to the hexagonal grid. Therefore, in Reference [15], a coordinate system with zero-sum and one-sum triplets are used to describe this grid (the three values are not independent, since this is also a 2D grid, see Figure 1c,d). The angle between any two of the three coordinate axes is 120◦ as for the hexagonal grid. In this

description, two pixels are neighbors if their coordinate values differ by at most one. Moreover, at the closest neighbors, only one coordinate differs [15,16]. Observe that exactly the same coordinate triplets are used on the left and on the right-hand side in Figure 1c,d, respectively, which shows the duality of the triangular and hexagonal grids. In Reference [17], a combinatorial coordinate system was shown that addresses the pixels, the corners, and the edges between them. That coordinate system gives an efficient tool to work with digital cell complexes on the triangular grid. Various image processing algorithms have been defined and implemented for the triangular grid recently such as discrete tomography [18,19], thinning [20], and mathematical morphology [21]. We should also mention that triangulation is a frequently used technique in imaging and computer graphics. However, the obtained grid is usually not the regular triangular grid. Lastly, we should note that several non-regular grids have various applications in 2D and in higher dimensions.

The coordination is vital because it is a leading tool in making a simple, easily usable, and effectively programmable system with integer numbers (coordinates). The isometric transformations are described in Reference [12]. However, up to now, there was no such extension of this coordinate system that is able to address the entire plane. For various applications, including, e.g., arbitrary angled rotations, an extension of the coordinate system is needed. We note here that Her's zero-sum triplets could match only up to half of the grid points in the triangular grid (Figure 1c,d), and, therefore, the coordinate system addressing the whole plain cannot be used for the triangular grid.

In this paper, we introduce a continuous coordinate system for the plain based on the symmetry of the triangular grid, where every point of the 2D plane has its unique coordinate triplet. We use three coordinate values to describe the triangular grid as in Reference [15] but also to address the points of the plane "between" and "around" the nodes of the dual grid. Our new coordinate system is shown to be an extension of the hexagonal and of the triangular coordinate systems. Moreover, our system builds upon the coordinate system for the so-called, tri-hexagonal grid, also called the three-plane triangular grid in Reference [16].

For further applications, we also provide a mapping between our continuous coordinate system for the triangular grid and the Cartesian coordinate system of the 2D plane.

The rest of this paper is as follows. The next, preliminary section, describes some important discrete coordinate systems and the barycentric coordinate system. The continuous coordinate system for the triangular grid is then introduced in Section 3. Conversions to and from Cartesian coordinates are also presented. Some important properties of the new coordinate system are presented in Section 4. Lastly, conclusions close the paper.
