Camera and Imaging
Explore how images are formed using camera lenses, image sensing and processing, and binary images.
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Course Description
- Discover imaging basics and innovations. Gain insights into the foundational aspects of imaging and explore recent advancements that have led to revolutionary changes in computer vision.
- Examine camera optics and sensor properties. Explore the optical traits of cameras, delve into solid-state image sensor attributes, including resolution, noise features, and dynamic range, and learn color sensing techniques.
- Master advanced image capture techniques. Learn to effectively capture images with high dynamic range and employ thresholding methods to create binary images crucial for precise object recognition and localization.
- Acquire image processing proficiency. Develop a strong grasp of image processing fundamentals to enhance captured images, making them more suitable for thorough analysis by computer vision systems.
Course Prerequisites
- A solid foundation in linear algebra and fundamental calculus
- Familiarity with any programming language will aid in comprehending the practical implementation of course methods in software
- Prior experience or knowledge in imaging or computer vision is not expected
What You Will Learn
By the end of this course, learners will be able to:
Grasp Versatile Imaging Fundamentals: Develop a profound understanding of imaging's core principles and its applications in computer vision, robotics, photography, virtual reality, and augmented reality.
Comprehend Camera Mechanics: Understand the inner workings of cameras, including image formation and the functioning of image sensors, along with their pivotal characteristics.
Design Advanced Imaging Systems: Gain the ability to design cameras optimized for capturing high dynamic range and wide-angle images, catering to various visual requirements.
Apply Image Processing Proficiency: Acquire skills in creating binary images for elementary object recognition, as well as developing techniques to elevate image quality through effective image processing.
Course Outline
Module 1: Introduction to first principles of computer vision
Module 2: Image formation
Module 3: Image sensing
Module 4: Binary Images
Module 5: Image processing I
Module 6: Image processing II
Instructors
Shree K. Nayar is the T.C. Chang Professor of Computer Science at Columbia University and leads the Columbia Vision Laboratory. His research focuses on developing advanced computer vision systems for digital imaging, computer graphics, robotics, and human-computer interfaces. He has received numerous awards and honors for his research and teaching, including the David Marr Prize, the David and Lucile Packard Fellowship, and the National Young Investigator Award. Nayar has also been elected to the National Academy of Engineering, the American Academy of Arts and Sciences, and the National Academy of Inventors. He holds a BS degree in Electrical Engineering from Birla Institute of Technology, an MS degree in Electrical and Computer Engineering from North Carolina State University, and a PhD degree in Electrical and Computer Engineering from the Robotics Institute at Carnegie Mellon University.
Please note that there are no instructors or course assistants actively monitoring this course.