In this chapter, we learned about leveraging some of the OpenCV-based techniques to identify contours, edges, and lines, and track colored objects. While we discussed a few use cases in this chapter, these techniques have a much broader application across the various use cases. Then, we learned about identifying similarities between two images using the keypoint and feature extraction techniques when stitching two images related to each other. Finally, we learned about cascade classifiers and leveraging the pre-trained ones to arrive at an optimal solution with little development effort, and also generating predictions in real time.
Broadly, through this chapter, we wanted to show that not all problems need neural networks and, especially in constrained environments, we can use a vast library of historical knowledge and techniques to quickly solve those problems. Where it is not possible to solve with OpenCV, we have already delved deep into neural networks.
Images are fascinating...
