So far, we looked at how to detect edges and corners in an image. Sometimes, for image analysis apart from edges and corners, you want to detect shapes, such as lines, circles, ellipses, or any other shape for that matter. Say for example, you want to detect coins in an image, or you want to detect a box or a grid in an image. A technique that comes handy in such scenarios is Hough transformations. It is a widely used technique that detects shapes in an image using their mathematical equations in their parameterized forms.

The generalized Hough transformation is capable of detecting any shape for which we can provide an equation in the parameterized form. As the shapes start getting complex (with an increase in the number of dimensions), such as spheres or ellipsoids, it gets computationally expensive; hence, we generally look at standard Hough transformations for simple 2D shapes, such as lines and circles.

In this section, we will take a look at Hough transformations...