Learning OpenCV

Invariant Features

Since the time of Harris's original paper and the subsequent work by Shi and Tomasi, a great many other types of corners and related local features have been proposed. One widely used type is the SIFT ("scale-invariant feature transform") feature [Lowe04]. Such features are, as their name suggests, scale-invariant. Because SIFT detects the dominant gradient orientation at its location and records its local gradient histogram results with respect to this orientation, SIFT is also rotationally invariant. As a result, SIFT features are relatively well behaved under small affine transformations. Although the SIFT algorithm is not yet implemented as part of the OpenCV library (but see Chapter 14), it is possible to create such an implementation using OpenCV primitives. We will not spend more time on this topic, but it is worth keeping in mind that, given the OpenCV functions we've already discussed, it is possible (albeit less convenient) to create most of the features reported in the computer vision literature (see Chapter 14 for a feature tool kit in development).