Left:What the car sees: The most advanced vision systems don't only point out obstacles, they identify them. This one from Mercedes uses a set of cameras and 3-D imaging to spot moving objects and predict their trajectories. It detects pedestrians by checking targets against its database of more than 1.5 million images of real and virtual people. (From Wired) Right: Google's self-driving car Waymo
Computer vision aims to develop methods that enable machines to gain the ability to analyze and understand the visual inputs. This undergraduate level course is intended for introducing the fundamental topics of computer vision. In this context, we will first start from the low-level image perception aspects, such as image formation, cameras, color and continue with mid-level vision topics, such as interest point detection and local feature extraction. The course will also introduce the fundamentals of high-level vision tasks, such as face detection/recognition, object recognition and human motion analysis.
Prerequisites: linear algebra, basic knowledge of probability and statistics, good programming skills. It is strongly recommended that you take BBM406 - Introduction to Machine Learning course before taking this class.
Instructor: Nazli Ikizler Cinbis , nazli -at- cs.hacettepe.edu.tr
Office: 112, Office Hours: by appointment via email
Lecture Hours: Tuesdays 13:00-15:45
Lecture Room: D8
Project: The project will involve designing and implementing a computer vision system towards solving a fundamental vision problem. The project can be carried out individually or in groups of two.
In the context of BBM418 Computer Vision Laboratory, the students are required to complete three programming assignments (%80) and there will be three quizzes (%20).
Date | Topic | Slides | Readings | Assignments |
Introduction to computer vision | .ppt | Szeliski 1 | ||
Image formation, camera and color | .ppt | Szeliski 2, FP 1.1,3.1,3.3 | ||
Filters, Templates and Image Pyramids | .ppt | Szeliski 3.2, FP 4 | ||
Interest points and image features | .ppt | Szeliski 4.1, FP 5 | ||
Edge Detection, line fitting | .ppt | Szeliski 4.1, FP 8 | ||
Machine learning: clustering and classification overview | .ppt | Szeliski 5 | ||
Midterm Exam | ||||
Instance recognition, bag-of-words models | .ppt | Szeliski 14.2, Szeliski 14.3, FP 16 | ||
Object detection with sliding windows | .ppt | Szeliski 14.1, FP 17 | ||
Motion analysis and Tracking | .ppt | FP 11 | ||
Human Action Recognition | .ppt | |||
Image Retrieval | .ppt | FP 21 | ||
Advanced Applications | .ppt | |||
Project presentations |