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The Basic Challenges in Computer Vision, Algorithms For Their Solution And Real Applications of These Algorithms. Using Deep Neural Networks For Image Understanding. Methods For Designing, Training and Understanding Neural Networks. Object Detection and Recognition, Image Segmentation. Generative Image Models. Image Alignment. Multiple View Geometry, Structure From Motion, Stereo, 3d Reconstruction. Motion Tracking. Learning Outcome# Upon Completing The Class The Student Will# 1. Understand The Geometry of Image Formation. 2. Understand The Desgn of Learning Algorithms For Visual Data. 3. Be Familiar With Advance Computer Vision Algorithms. 4. Gain Hands On Experience With State-of-the-art Computer Vision Code Libraries.

Faculty: Electrical and Computer Engineering
|Undergraduate Studies |Graduate Studies

Pre-required courses

(46195 - Machine Learning and 46200 - Image Processing and Analysis) or (46200 - Image Processing and Analysis and 236756 - Introduction to Machine Learning)

Course with no extra credit

236873 - Computer Vision

• Computer vision : algorithms and applications - Szeliski, Richard
• Computer Vision Algorithms and Applications - Szeliski, Richard.
• Multiple view geometry in computer vision - Hartley, Richard
• Multiple view geometry in computer vision - Hartley, Richard.