ראייה ממוחשבת

Abstract

This course provides an introduction to computer vision, an area that lies in the heart of many modern AI applications: robotics navigation, searching through billions of images, autonomous vehicles and social networking. Computer vision technologies can understand and reconstruct the visual world. The course covers a range of topics, including feature detection, motion estimation, panoramas, 3D shape reconstruction, and object detection and recognition.

This course emphasizes hands-on experience with computer vision, with several large programming projects.

Prerequisites

Knowledge of the following is required:

• Image Processing and Analysis 046200
• Working knowledge of MATLAB or Python and preferably also C/C++
• Linear algebra
• Basic probability theory and statistics
• Recommended: Vector calculus, Data structures

Textbook

The course will have readings from Computer Vision: Algorithms and Applications (available online), by Richard Szeliski.

Grading

The course grade will be determined as follows:

• HW1: 15%
• HW2: 15%
• HW3: 15%
• HW4: 10%
• Active participation in class: 5%
• Final exam: 40%