Foreword

Over the last few years, our machines have slowly but surely learned how to see for themselves. We now take it for granted that our cameras detect our faces in pictures that we take, and that social media apps can even recognize us and our friends in the photos that we upload from these cameras. Over the next few years we will experience even more radical transformation. Before long, cars will be driving themselves, our cellphones will be able to read and translate a sign in any language for us, and our x-rays and other medical images will be read and analyzed by powerful algorithms that will be able to accurately suggest a medical diagnosis, and even recommend effective treatments.

These transformations are driven by an explosive combination of increased computing power, masses of image data, and a set of clever ideas taken from math, statistics, and computer science. This rapidly growing intersection that is machine learning has taken off, affecting many of our day-to-day interactions with the world, and with each other. One of the most remarkable features of the current machine learning paradigm-shift in computer vision is that it relies to a large extent on software tools that are freely available and developed by large groups of volunteers, hobbyists, scientists, and engineers in open source communities. This means that, in principle, the barriers to entry are also lower than ever: anyone who is interested in putting their mind to it can harness machine learning for image processing.

However, just like in a garden with many forking paths, the wealth of tools and ideas, and the rapid development of these ideas, underscores the need for a guide who can show you the way, and orient you in the right direction. I have some good news for you: having picked up this book, you are in the good hands of my colleague and collaborator Dr. Michael Beyeler as your guide. With his broad range of expertise, Michael is both a hard-nosed engineer, computer scientist, and neuroscientist, as well as a prolific open source software developer. He has not only taught robots how to see and navigate through complex environments, and computers how to model brain activity, but he also regularly teaches humans how to use programming to solve a variety of different machine learning and image processing problems. This means that you will get to benefit not only from the sure-handed rigor of his expertise and experience, but also that you will get to enjoy his thoughtfulness in teaching the ideas in his book, as well as a good dose of his sense of humor.

The second piece of good news is that this going to be an exhilarating trip. There's nothing that matches the thrill of understanding that comes from putting together the pieces of the puzzle that go into solving a problem in computer vision and machine learning with code and data. As Richard Feynman put it: "What I cannot create, I do not understand". So, get ready to get your hands dirty (so to speak) with the code and data in the (open source!) code examples that accompany this book, and to get creative. Understanding will surely follow.