This chapter covered the key aspects of computer vision's daily use. We started with the important edge detectors, where you gained the experience of how to find them through the Sobel, Laplacian, and Canny edge detectors. Then, we saw how to use the Hough transforms to find straight lines and circles. After that, the geometric transforms stretch, shrink, warp, and rotate were explored with an interactive sample. We then explored how to transform images from the spatial domain to the frequency domain using the Discrete Fourier analysis. After that, we showed you a trick to calculate Haar-like features fast in an image through the use of integral images. We then explored the important distance transforms and finished the chapter by explaining histogram equalization to you.
Now, be ready to dive into machine learning algorithms, as we will cover how to detect faces in the next chapter. Also, you will learn how to create your own object detector and understand how supervised learning...
