Instructor: Ali Seydi keçeli
Course Content:
This course provides an introduction to computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, and deep learning with neural networks. We will develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, boundary detection, and recognition. We will develop the intuitions and mathematics of the methods in class, and then learn about the difference between theory and practice in homeworks.
Week Topic
1- Introduction
2- Image Formation
3- Filtering, Edge Detection
4- Hough Transform
5- Corner Detection, Features, SIFT
6- Camera Fundamentals, Camera Calibration
7- Simple Stereo, Homography
8- Neural Networks
9- Deep Learning Fundamentals
10- CNNs, Transfer Learning
11- Object Detection, Viola Jones, R-CNN, YOLO
12 - Semantic Segmentation
13- Sequential Models, RNN,
14- Optical Flow
Text Book: Computer Vision: Algorithms and Applications, 2nd ed. https://szeliski.org/Book/
Homework 1 30%
Homework 2 30%
Research project 40%
Logistics:
All communication and announcements will be posted in Piazza Page