Computer vision (CV) is the discipline of “teaching machines how to see”: it makes sense of photographs, video, and other imagery. Applications include analysis of medical images, automated quality inspection, entertainment, vehicle safety, security, and HCI, among many others. This course offers a gentle introduction to computer vision, including image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. Both classical and the latest deep learning approaches will be covered.
The students will digest and practice their knowledge and skills by both homework and a midterm exam. They will also obtain in-depth experience with a particular topic through a final project. There will be no final exam.
Students should have taken the following courses or equivalent: Algorithms (EE 360C or CS 314/314H), Linear Systems and Signals (EE313 or BME 343), Probability and Random Processes (EE 351K or BME 335 or MATH 362K). Solid Knowledge of Linear Algebra will be instrumental to this course.
Coding experiences with Python are assumed. Previous knowledge of C/C++, MATLAB, or PyTorch/Tensorflow is very helpful, but not necessary.
This course does not follow any textbook closely. Among many recommended readings are:
Grading will be based on homework (20%; there will be 4 assignments), one mid-term exam (30%), and one final project (50%) (proposal 10% + mid-report 10% + presentation 5% + final report 15% + code review 10%).
Many materials included in this course are adapted from the existing teaching or tutorial slides, created by colleagues in CMU, Stanford, UIUC, UC Berkeley, GaTech, Brown, and more. The instructor owes many thanks for their generosity of sharing those materials publicly.
