This document contains course notes for a Computer Vision class in Fall 2018. It outlines the topics to be covered each week, including introductions to computer vision and image formation, template matching, Matlab tutorials, and image filtering. It details the assessment breakdown as 10% midterm, 10% final, 15% quizzes, and 65% assignments. Reading materials suggested include books by Szeliski, Prince, Shah, Forsyth and Ponce, and Hartley and Zisserman.
This document contains course notes for a Computer Vision class in Fall 2018. It outlines the topics to be covered each week, including introductions to computer vision and image formation, template matching, Matlab tutorials, and image filtering. It details the assessment breakdown as 10% midterm, 10% final, 15% quizzes, and 65% assignments. Reading materials suggested include books by Szeliski, Prince, Shah, Forsyth and Ponce, and Hartley and Zisserman.
This document contains course notes for a Computer Vision class in Fall 2018. It outlines the topics to be covered each week, including introductions to computer vision and image formation, template matching, Matlab tutorials, and image filtering. It details the assessment breakdown as 10% midterm, 10% final, 15% quizzes, and 65% assignments. Reading materials suggested include books by Szeliski, Prince, Shah, Forsyth and Ponce, and Hartley and Zisserman.
This document contains course notes for a Computer Vision class in Fall 2018. It outlines the topics to be covered each week, including introductions to computer vision and image formation, template matching, Matlab tutorials, and image filtering. It details the assessment breakdown as 10% midterm, 10% final, 15% quizzes, and 65% assignments. Reading materials suggested include books by Szeliski, Prince, Shah, Forsyth and Ponce, and Hartley and Zisserman.
Szeliski Book 03-09-18 ● Other useful books 1. Computer Vision: Models, Learning, and Inference by Simon J.D. Prince 2. FUNDAMENTALS OF COMPUTER VISION(. Mubarak Shah. 3. Forsyth, David A., and Ponce, J. Computer Vision: A Modern Approach, Prentice Hall, 2003. 4. Hartley, R. and Zisserman, A. Multiple View Geometry in Computer Vision, Academic Press, 2002.
Introduction to ● What is Computer Vision?
Computer Vision ● Why we need it? ● The Complexity of Perception ● Applications of Computer Vision
Image Formation Image formation
● Pinhole camera ● Camera Obscura ● Problems of pinhole camera ● Lens camera 06-09-18 ● Camera and World Geometry ● Dimensionality Reduction (3D to 2D) ● Projective Geometry ● Vanishing points and lines ● Quantization and Sampling ● What is the Digital Image? 1. Representation 2. Generation How Pinhole camera works Readings: Szeliski: Chapter 2.2, 2.3 Mubarak Shah: Chapter 1.5 ● Picture understanding by computers
10-09-18 Template Matching ● Images as functions
● Some Matlab ● Sum of Squared Distance ● Template Matching ● SSD for template matching ● Relationship between SSD and Correlation ● Cross Correlation Readings ● Szeliski: Chapter A.1.1, A.1.2,A.2,A.3
Introduction to ● Introduction to Matlab (Code)
11-09-18 Matlab and ● Introducing Matlab by Lazebnik Lab Task 1 MATLAB Tutorial ● Writing Fast MATLAB Code by Pascal Getreuer