This set of lectures introduces what an image consists of, from a computer's point of view, how the computer can capture the image, what can go wrong, some of the different types of information in an image, what makes image analysis hard, and the mathematics and geometry of image projection. Then, exploiting the geometry and mathematics, we introduce the concept of homography and show how one can use a homography to map image data from one image to another.
This lecture introduces what an image consists of, from a computer's point of view, how the computer can capture the image, \ what can go wrong, some of the different types of information in an image, and what makes image analysis hard.
This lecture introduces the basics of light, how it interacts with the world, how it is captured by a sensor, and how it can be encoded. There is also some discussion of human colour perception.
Analysing an image usually requires a high quality image, but there are many factors that lead to poor quality images, such as focus, shadows, saturation, non-uniform illumination, and lens distortion. This lecture introduces the problems and suggests some approaches to overcoming them.
This lecture introduces the pinhole camera model, and how it can be represented and used in 3D coordinate geometry, including homogeneous coordinates.
This lecture introduces some examples of the distribution of intensity values and also some simple Matlab for loading images and then computing histograms of the images.
This lecture ties together image geometry and computation. It introduces the mathematical concept of homography, which can be used to map between planar regions, which we use here to transfer a picture from a plane within the image to yet another plane.