In this chapter, we learned how image filtering modifies the input image through a convolution operation to produce an output that detects a portion of a feature called an edge. This is fundamental to computer vision. As you will learn in the following chapters, subsequent application of image filtering will transform the edges to a higher-level pattern, such as features.
We also learned how to calculate an image histogram, perform image matching using SIFT, and use contour and the HOG detector to draw a bounding box. We learned how to use OpenCV's bounding box color and size method to segregate one class from another. The chapter concluded with an introduction to TensorFlow, which will provide a foundation for the remaining chapters of this book.
In the next chapter, we will learn about a different type of computer vision technique, called pattern recognition, and we will use it to classify the contents of an image with a pattern.
