Introduction

The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos.

This course is an introduction to current algorithms used in computer vision and computational photography (automatic image editing and manipulations). We will start from low-level image processing (edges), and then move to mid-level feature analysis (texture, color, motion), and eventually to high-level image and video understanding (objects, faces, scene, human activity). The topics include basic image processing and image analysis, camera models, texture synthesis, motion analysis, automatic image editing, object and scene recognition, face and pose recognition and a gentle survey of deep learning methods for computer vision. We will develop the intuitions and mathematics of the methods in class, and then learn about the difference between theory and practice in projects.

We will use Python as our primary programming tool in this course. Codes written in C and MATLAB might be demoed if needed. Working models of computer vision will be demoed in class and homework will be programming exercises with tools such as Numpy, SciPy, and OpenCV.