Local feature representations

A relatively recent development in the computer vision world has been the development of local-feature-based methods. Local features are computed on a small region of the image, unlike the previous features we considered, which had been computed on the whole image. Mahotas supports computing a type of these features; Speeded Up Robust Features, also known as SURF (there are several others, the most well-known being the original proposal of Scale-Invariant Feature Transform (SIFT)). These local features are designed to be robust against rotational or illumination changes (that is, they only change their value slightly when illumination changes).

When using these features, we have to decide where to compute them. There are three possibilities that are commonly used:

• Randomly

• In a grid

• Detecting interesting areas of the image (a technique known as keypoint detection or interest point detection)

All of these are valid and will, under the right circumstances, give good...