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Perspective Projection of a Rectangle (Homography)

, 26 Oct 2013 CPOL 10.2K 710 9
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Short study of the perspective projection of a rectangle in space; homography opposed to bilinear transform


A rectangle in general position in space is seen by a camera as a quadrilateral. Any point inside the rectangle projects to a point inside the quadrilateral. This article is about how the mapping of any point can be found, given the projections of the four corners.

We will establish the necessary equations, to compare to the well-known bilinear interpolation method and highlight the inaccuracy of the latter for this purpose.

The Bilinear Transform

Let us discuss the bilinear transform first. A very simple way to map the original rectangle to the quadrilateral is by linear interpolation along the sides.

First we perform a linear interpolation along two opposite sides, using an interpolation parameter . Then we interpolate between these two interpolated points, using a second interpolation parameter, . Given the four projected corners , clockwise, we have:


or simply:

You can check for yourself a nice property of this scheme: if you start interpolating on the other pair of sides (03, 12) instead of (01, 32), you will get exactly the same result.

Note that by this definition, any grid line with or constant is transformed into a straight line. (But arbitrary lines are transformed into a curve - a conic curve.)

The Homographic Transform

Establishing the true projection equations will require some more effort. First, we will establish the general form of the equations, then the way to compute any unknown parameter they comprise.

From spatial geometry, we know that a rigid object is moved in space by applying to it a rotation and a translation. A rotation is described by a 3x3 matrix which is applied to the original coordinates, and the translation by a 3-vector which is added. In our case, the rectangle lies in a plane so that one of the input coordinates is identically 0.

Hence the transformation equations:

Then the projection itself is achieved by reducing the coordinates in the inverse proportion of the distance:

with being a constant called the focal length of the camera, that we can absorb in the constants above.

We obtain a so-called homography:

The nine coefficients can be multiplied by a common arbitrary factor, and without loss of generality, we can assume , to get:

To determine the 8 unknown coefficients, we use the coordinates of the projections of the four corners, let , corresponding to and , in clockwise order and replace them in the general expression:

The bad news: this gives us a nonlinear system of 8 equations in 8 unknowns ( to ), something usually painful to solve.

The good news: the system can be linearized and simplified to such an extent that the solution becomes straightforward.

First, we translate all by so that becomes the origin; we denote the new vertices . This makes the coefficients and vanish because:

Two unknowns are gone!

Next, we linearize by moving the denominator to the left-hand side and rearranging the terms:

When we substitute by their values at , and , we get these six equations:

By introducing , and , we get a further simplification (this is a little rabbit out of a hat – but it is optional):

and we subtract equations 1 and 5 from 3, and 2 and 6 from 4, yielding (note the new indexes):

Solving this 2x2 system in and by Cramer's rule is trivial, and , , , , , immediately follow.

The accompanying demo application will show you the discrepancies between the bilinear and perspective transforms, which is particularly noticeable for skewed quadrilaterals. On the opposite, for parallelograms both transforms become affine and coincide exactly.

When the quadrilateral is not convex, the perspective transform shows "erratic" behavior. This is because such cases are not physically achievable.


General understanding of analytic geometry and perspective transforms is assumed.

Using the Code

The code directly reflects the equations established in the article and is just meant to illustrate these. The chosen language is irrelevant.

Drag the quadrilateral corners with the mouse.

Points of Interest

While working on the homographic equations, I was amazed to see how easily they can be solved in the case of a rectangle.


This is the first version.


This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


About the Author

Belgium Belgium
I fell into applied algorithmics at the age of 16 or so. This eventually brought me to develop machine vision software as a professional. This is Dreamland for algorithm lovers.

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