“‘Our sight is the most perfect and the most delightful of all our senses,’ said poet Joseph Addison. Building machines that can see is the goal of computer vision, and we have already witnessed its successful applications like face recognition and driverless cars. But there is so much more to come in the next decade that will profoundly impact our lives.
In this lecture series, we will explore the mathematical and physical underpinnings of computer vision. By understanding how images are formed and developing various methods to extract information from them, we can unlock the potential of computer vision. Throughout the series, we will explore real-world applications that demonstrate the power of vision.
You might be wondering if learning the first principles of vision is necessary when deep learning is so popular today. While deep learning can solve many tasks, knowing the basics is still important for several reasons. First, some phenomena can be precisely described using first principles, making it unnecessary to rely solely on neural networks. Second, understanding first principles becomes invaluable when a network underperforms. Third, synthesizing data using models based on first principles can be an alternative to collecting tedious or impractical datasets. And finally, learning the first principles of any field satisfies our inherent curiosity about how things work.
This lecture series is divided into five modules, each covering an important aspect of computer vision. No prior knowledge of computer vision is required; familiarity with linear algebra, calculus, and a programming language will be useful.
While we approach computer vision as an engineering discipline in this series, we also draw connections to other fields like neuroscience, psychology, art history, and biology when relevant. Our aim is to make these lectures enjoyable and to convince you that computer vision is not only powerful but also fascinating.
Join us on this journey to uncover the immense potential of computer vision!
Contents
Module 1: Introduction to Computer Vision
In this module, we lay the foundation by introducing the fundamental concepts and principles of computer vision. We will explore the basics of image formation and delve into methods to extract information from images. This module will provide you with a solid understanding of the building blocks of computer vision.
Module 2: Image Processing and Analysis
Building upon the knowledge gained in Module 1, we dive deeper into image processing and analysis techniques. We will explore methods such as filtering, segmentation, and feature extraction that allow us to extract valuable information from images. By the end of this module, you will be equipped with the skills to manipulate and analyze images effectively.
Module 3: Object Detection and Recognition
In this module, we focus on the exciting field of object detection and recognition. You will learn about different approaches to detect objects in images and understand how to recognize and classify those objects. We will cover both traditional methods and state-of-the-art deep learning techniques, giving you a comprehensive understanding of object detection and recognition.
Module 4: 3D Vision
Module 4 takes us into the realm of 3D vision, where we explore techniques to understand the three-dimensional structure of the world from two-dimensional images. We will cover topics such as camera calibration, stereo vision, and depth estimation. By the end of this module, you will be able to perceive and reconstruct the 3D world from images.
Module 5: Advanced Topics in Computer Vision
In the final module, we dive into advanced topics in computer vision, including motion analysis, tracking, and scene understanding. We will explore how to analyze and interpret video sequences, enabling us to extract valuable information about object motion and scene dynamics. This module will equip you with the skills to tackle complex computer vision problems.
FAQs
Q: Do I need prior knowledge of computer vision to take this lecture series?
A: No, you don’t need any prior knowledge of computer vision. However, a familiarity with linear algebra, calculus, and programming will be helpful.
Q: Can I implement the methods described in these lectures in software?
A: Yes, if you have knowledge of a programming language, you can easily implement the methods discussed in these lectures.
Q: Will this lecture series cover deep learning in computer vision?
A: While deep learning will be mentioned when relevant, this lecture series focuses on the mathematical and physical foundations of computer vision, rather than being solely dedicated to deep learning techniques.
Conclusion
We hope you enjoy this lecture series on computer vision. By the end of it, we aim to convince you of the immense power and fascination that computer vision holds. Join us on this journey to unlock the potential of computer vision and understand the world through the eyes of machines.
