Summary
In this chapter, we learned about detecting keypoints, computing keypoint descriptors, matching these descriptors, filtering out bad matches, and finding the homography between two sets of matching keypoints. We explored a number of algorithms that are available in OpenCV that can be used to accomplish these tasks, and we applied these algorithms to a variety of images and use cases.
If we combine our new knowledge of keypoints with additional knowledge about cameras and perspective, we can track objects in 3D space. This will be the topic of Chapter 9, Camera Models and Augmented Reality. You can skip ahead to that chapter if you are particularly keen to reach the third dimension.
If, instead, you think the next logical step is to round off your knowledge of two-dimensional solutions for object detection, recognition, and tracking, you can continue sequentially with Chapter 7, Building Custom Object Detectors, and then Chapter 8, Tracking Objects. It is good to know of a combination...
