In most images, the most useful information is around certain zones that typically correspond to salient points and regions. In most applications, local processing around these salient points is sufficient as long as these points are stable and distinctive. In this chapter, we will cover a basic introduction to the 2D salient points and features offered by OpenCV. It is important to note the difference between detectors and descriptors. Detectors only extract interest points (local features) on an image, while descriptors obtain relevant information about the neighborhood of these points. Descriptors, as their name suggests, describe the image by proper features. They describe an interest point in a way that is invariant to change in lighting and to small perspective deformations. This can be used to match them with other descriptors (typically extracted from other images). For this purpose, matchers are used. This, in turn, can be used to detect objects and infer the camera transformation between two images. First, we show the internal structure of the interest points and provide an explanation of the 2D features and descriptor extraction. Finally, the chapter deals with matching, that is, putting 2D features of different images into correspondence.