An Introduction to Camera Calibration in Computer Vision (Part 1/5)
Bridging the gap between images and real-world coordinates
Introduction
Camera calibration is a fundamental process in computer vision that enables the recovery of three-dimensional (3D) structure from images. It involves determining the internal and external parameters of a camera, which allows us to map pixels in an image to their corresponding points in the real world.
In this blog post, we will introduce the concept of camera calibration, discuss camera models, and explore a simple application in stereo vision.
Camera Models and Linear Projection
To estimate the camera’s internal and external parameters, we first need a camera model. A camera model, also known as a forward imaging model, describes how a 3D point is projected onto the image plane in pixels. We prefer a linear camera model since estimating linear models is much simpler than estimating nonlinear models. The linear model we use is represented by a single matrix called the projection matrix.
Camera Calibration and the Projection Matrix
Camera calibration is the process of determining the projection matrix, which encapsulates both the internal (intrinsic) and external (extrinsic) parameters of the camera. By taking a single picture of an object with known geometry, we can fully calibrate the camera and determine the projection matrix. Once the projection matrix is obtained, we can decompose it to find both the intrinsic and extrinsic matrices, effectively calibrating the camera.
Simple Stereo
Calibrated Camera Application
With a calibrated camera, we can reconstruct 3D scenes from images. One simple application is stereo vision, which involves two identical, horizontally displaced cameras capturing the same scene. By assuming that both cameras are calibrated, we can use the disparity between corresponding points in the two images to recover a 3D representation of the scene.
Conclusion
Camera calibration is a crucial step in computer vision, enabling the transformation of image pixels into real-world coordinates. By understanding camera models, the projection matrix, and calibration techniques, we can reconstruct 3D scenes from images and open up a wide range of applications in fields such as robotics, augmented reality, and autonomous vehicles.
In future posts, we will delve deeper into camera calibration techniques, stereo vision, and other computer vision topics.
I would like to thank Dr. Shree Nayar, First Principles of Computer Vision Specialization, This article is based on my learning from a faculty member in the Computer Science Department at the School of Engineering and Applied Sciences, Columbia University.
Thank you for taking the time to read! Don’t forget to 👏 if you liked the article.
A Note on This Article
The insights and perspectives shared in this article are drawn from my personal experiences. As with any subjective matter, there may be differing viewpoints or approaches.
If you have any questions, concerns, or alternative perspectives to offer, I’d be glad to hear from you. An open dialogue allows us all to gain a deeper understanding of the topic at hand.
Feel free to share your thoughts or feedback in the comments below or reach out to me directly. I’m always eager to learn and grow through respectful discourse.
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