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Public Lab Research note

GSoC Update- Working of Image Distortion In Mapknitter

by anishshah101 | June 13, 2014 06:17 13 Jun 06:17 | #10557 | #10557

anishshah101 was awarded the Basic Barnstar by anishshah101 for their work in this research note.

The image distortion in the editing interface in Mapknitter is one of the complex operations taking place in the tool.

This steps which take place when we are distorting an image are: 1) The image gets unlocked to allow editing on it.


2) The corners of the image(also known as control points) are dragged by the user to allow distortion.


3) When the control points are dragged, the image gets divided into a large number of small triangles and on each of these triangles, affine transformation takes place. As a result of many affine transformations and some end corrections, it appears as if the image as a whole is undergoing non-affine transformation or more specifically projective transformation.

In step 3, since each triangle will have a different plane of transformation, it is not possible to find a simple unique rule common to transforming all the triangles. To overcome this drawback, the Cartesian coordinates of the triangle are converted into homogeneous coordinates which helps to transform the triangles with a common rule.


Is there any logic to how the image gets distorted? I find that it is useful to think about, or I am crippled with the possibilities.

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It is difficult to form an equation which does non-affine transformations. However, it is easy to have equations which can do affine transformations. The current interface works on this principle and so it divides the image into a number of triangles and does affine transformations on them. Due to a large number of triangles undergoing simpler transformations, the image as a whole appears to have undergone a complex non-affine transformation. Here is how it works:

We can imagine a parallelogram having 3 edges fixed and one edge being dragged. When this happens, we can visualize that only half of the parallelogram will get affected by this event i.e. the triangle formed by joining the edge being moved and it's neighboring edges. The other half will remain unaltered. To solve this, it is further broken down into more number of triangles so that the effect of the movement of an edge gets propagated throughout. (Credits: btbonval)

This link: can help understand how the image gets divided into triangles and how the accuracy of transformation varies by changing the subdivision factor.

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This is a well written summary, Anish.

@eustatic There is a logic to the distortion. As Anish said, the end result of these affine transformations looks like a projective distortion. The projective distortion is the logic. It is a specific kind of distortion that is calculated, in this case, given the four control points.

I think it is implied in step 3 that the control points drive the projective distortion, but that fact could be made more clear.

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