The SURF and SIFT keypoint detection algorithms, discussed in Chapter 8, Detecting Interest Points, define a location, an orientation, and a scale for each of the detected features. The scale factor information is useful to define the size of a window of analysis around each feature point. Thus, the defined neighborhood would include the same visual information no matter what the scale of the object to which the feature belongs has been pictured. This recipe will show you how to describe an interest point's neighborhood using feature descriptors. In image analysis, the visual information included in this neighborhood can be used to characterize each feature point in order to make each point distinguishable from the others. Feature descriptors are usually N-dimensional vectors that describe a feature point in a way that is invariant to change in lighting and to small perspective deformations. Generally, descriptors can be compared using simple distance...