In the previous sections, we covered the basics of computer vision techniques, such as image conversion, image filtering, convolution using a kernel, edge detection, histograms, and feature matching. This understanding and its various applications should develop a solid foundation for the advanced concept of deep learning, which will be introduced later on in this book.
Deep learning in computer vision is the cumulative learning of many different image features (such as edges, colors, boundaries, shapes, and so on) through a convolution operation of many intermediate (hidden) layers to gain a complete understanding of the image type. Deep learning augments computer vision techniques because it stacks many layers of calculations about how neurons behave. This is done by combining various inputs to produce outputs based...