An image of a fern-like fractal that exhibits affine self-similarity. Each of the leaves of the fern is related to each other leaf by an affine transformation. For instance, the red leaf can be transformed into both the dark blue leaf and the light blue leaf by a combination of reflection, rotation, scaling, and translation.
In geometry, an affine transformation, affine map or an affinity (from the Latin, affinis, "connected with") is a function between affine spaces which preserves points, straight lines and planes. Also, sets of parallel lines remain parallel after an affine transformation. An affine transformation does not necessarily preserve angles between lines or distances between points, though it does preserve ratios of distances between points lying on a straight line.
If and are affine spaces, then every affine transformation is of the form , where is a linear transformation on the space , is a vector in , and is a vector in . Unlike a purely linear transformation, an affine map need not preserve the zero point in a linear space. Thus, every linear transformation is affine, but not every affine transformation is linear.
An affine map between two affine spaces is a map on the points that acts linearly on the vectors (that is, the vectors between points of the space). In symbols, determines a linear transformation such that, for any pair of points :
We can interpret this definition in a few other ways, as follows.
If an origin is chosen, and denotes its image , then this means that for any vector :
If an origin is also chosen, this can be decomposed as an affine transformation that sends , namely
followed by the translation by a vector .
The conclusion is that, intuitively, consists of a translation and a linear map.
Given two affine spaces and , over the same field, a function is an affine map if and only if for every family of weighted points in such that
As shown above, an affine map is the composition of two functions: a translation and a linear map. Ordinary vector algebra uses matrix multiplication to represent linear maps, and vector addition to represent translations. Formally, in the finite-dimensional case, if the linear map is represented as a multiplication by a matrix and the translation as the addition of a vector , an affine map acting on a vector can be represented as
Affine transformations on the 2D plane can be performed in three dimensions. Translation is done by shearing along over the z axis, and rotation is performed around the z axis.
Using an augmented matrix and an augmented vector, it is possible to represent both the translation and the linear map using a single matrix multiplication. The technique requires that all vectors are augmented with a "1" at the end, and all matrices are augmented with an extra row of zeros at the bottom, an extra column--the translation vector--to the right, and a "1" in the lower right corner. If is a matrix,
Ordinary matrix-vector multiplication always maps the origin to the origin, and could therefore never represent a translation, in which the origin must necessarily be mapped to some other point. By appending the additional coordinate "1" to every vector, one essentially considers the space to be mapped as a subset of a space with an additional dimension. In that space, the original space occupies the subset in which the additional coordinate is 1. Thus the origin of the original space can be found at . A translation within the original space by means of a linear transformation of the higher-dimensional space is then possible (specifically, a shear transformation). The coordinates in the higher-dimensional space are an example of homogeneous coordinates. If the original space is Euclidean, the higher dimensional space is a real projective space.
The invertible affine transformations (of an affine space onto itself) form the affine group, which has the general linear group of degree as subgroup and is itself a subgroup of the general linear group of degree .
Each of these groups has a subgroup of orientation-preserving or positive affine transformations: those where the determinant of is positive. In the last case this is in 3D the group of rigid body motions (proper rotations and pure translations).
If there is a fixed point, we can take that as the origin, and the affine transformation reduces to a linear transformation. This may make it easier to classify and understand the transformation. For example, describing a transformation as a rotation by a certain angle with respect to a certain axis may give a clearer idea of the overall behavior of the transformation than describing it as a combination of a translation and a rotation. However, this depends on application and context.
In their applications to digital image processing, the affine transformations are analogous to printing on a sheet of rubber and stretching the sheet's edges parallel to the plane. This transform relocates pixels requiring intensity interpolation to approximate the value of moved pixels, bicubic interpolation is the standard for image transformations in image processing applications. Affine transformations scale, rotate, translate, mirror and shear images as shown in the following examples:
The affine transforms are applicable to the registration process where two or more images are aligned (registered). An example of image registration is the generation of panoramic images that are the product of multiple images stitched together.
The affine transform preserves parallel lines. However, the stretching and shearing transformations warp shapes, as the following example shows:
This is an example of image warping. However, the affine transformations do not facilitate projection onto a curved surface or radial distortions.
In the plane
A central dilation. The triangles A1B1Z, A1C1Z, and B1C1Z get mapped to A2B2Z, A2C2Z, and B2C2Z, respectively.
Affine transformations in two real dimensions include:
scaling in a given direction, with respect to a line in another direction (not necessarily perpendicular), combined with translation that is not purely in the direction of scaling; taking "scaling" in a generalized sense it includes the cases that the scale factor is zero (projection) or negative; the latter includes reflection, and combined with translation it includes glide reflection,
To visualise the general affine transformation of the Euclidean plane, take labelled parallelogramsABCD and A?B?C?D?. Whatever the choices of points, there is an affine transformation T of the plane taking A to A?, and each vertex similarly. Supposing we exclude the degenerate case where ABCD has zero area, there is a unique such affine transformation T. Drawing out a whole grid of parallelograms based on ABCD, the image T(P) of any point P is determined by noting that T(A) = A?, T applied to the line segment AB is A?B?, T applied to the line segment AC is A?C?, and T respects scalar multiples of vectors based at A. [If A, E, F are collinear then the ratio length(AF)/length(AE) is equal to length(A?F?)/length(A?E?).] Geometrically T transforms the grid based on ABCD to that based in A?B?C?D?.
Affine transformations do not respect lengths or angles; they multiply area by a constant factor
area of A?B?C?D? / area of ABCD.
A given T may either be direct (respect orientation), or indirect (reverse orientation), and this may be determined by its effect on signed areas (as defined, for example, by the cross product of vectors).
Over the real numbers
Functions with and constant, are commonplace affine transformations.
Over a finite field
The following equation expresses an affine transformation in GF(28):
where is the matrix and is the vector
For instance, the affine transformation of the element in big-endianbinary notation = in big-endian hexadecimal notation, is calculated as follows:
In plane geometry
A simple affine transformation on the real plane
Effect of applying various 2D affine transformation matrices on a unit square. Note that the reflection matrices are special cases of the scaling matrix.
In R2, the transformation shown at left is accomplished using the map given by:
Transforming the three corner points of the original triangle (in red) gives three new points which form the new triangle (in blue). This transformation skews and translates the original triangle.
In fact, all triangles are related to one another by affine transformations. This is also true for all parallelograms, but not for all quadrilaterals.
Anamorphosis - artistic applications of affine transformations
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