A first-principles introduction to the acquisition and computational processing of 2D images, aimed at students interested in computer vision, digital photography and computer graphics. The course serves as a stepping stone for tackling more advanced courses in those subjects and covers four broad themes: (1) mathematical and engineering foundations: introducing key concepts from geometry; multivariate calculus; linear algebra; image and signal processing; and human vision, (2) algorithms for low-level computer vision: image warping, morphing and stitching; image enhancement; image scissoring and inpainting; color image processing and display; face recognition; and 2D image matching, (3) implementing several such tools in Python, and (4) a first taste of vision and graphics research. Understanding how to turn algorithmic descriptions in research papers into working computer vision code—and how to evaluate its performance—will be key skills acquired in the course.
A first-principles introduction to the acquisition and computational processing of 2D images, aimed at students interested in computer vision, digital photography and computer graphics. The course serves as a stepping stone for tackling more advanced courses in those subjects and covers four broad themes: (1) mathematical and engineering foundations: introducing key concepts from geometry; multivariate calculus; linear algebra; image and signal processing; and human vision, (2) algorithms for low-level computer vision: image warping, morphing and stitching; image enhancement; image scissoring and inpainting; color image processing and display; face recognition; and 2D image matching, (3) implementing several such tools in Python, and (4) a first taste of vision and graphics research. Understanding how to turn algorithmic descriptions in research papers into working computer vision code—and how to evaluate its performance—will be key skills acquired in the course.
