Spring 2024: CS 4391 Introduction to Computer Vision

Understand the 3D world from 2D images

Course Information

Term: Spring 2024
Class Level: Undergraduate
Activity Type: Lecture
Days & Times: Tuesday & Thursday 11:30 AM – 12:45 PM
Location: JO 4.102

Instructor: Prof. Yu Xiang
Office Location: ECSS 4.702
Office Hours: Tuesday & Thursday 2:00PM – 3:00PM

Teaching Assistant: Jishnu P
Office Hours: Monday 12:00PM – 1:00PM

Syllabus

All the course materials can be found here.

Course Description

Theory and practice of computer vision. Provides in-depth overview of computer vision, including geometric primitives and transformations, camera models, image features, epipolar geometry and stereo, structure from motion and SLAM, 3D reconstruction, variations of modern neural networks and various recognition problems such as object detection, semantic segmentation, and human pose estimation.

Textbooks

Richard Szeliski. Computer Vision: Algorithms and Applications. 2011th Edition. Springer.
ISBN-13: 978-1848829343
ISBN-10: 1848829345
Second Edition

David Forsyth, Jean Ponce. Computer Vision: A Modern Approach, 2nd Edition. Pearson, 2011. (Optional)
ISBN: 9789332550117

Richard Hartley. Multiple View Geometry in Computer Vision, 2nd Edition. Cambridge University Press, 2004. (Optional)
ISBN-13: 978-0521540513
ISBN-10: 0521540518

Grading Policy

  • Homework (50%)
    • Assignment 1 (10%)
    • Assignment 2 (10%)
    • Assignment 3 (10%)
    • Assignment 4 (10%)
    • Assignment 5 (10%)
  • Midterm Exam (20%)
  • Final Exam (25%)
  • In-Class Activity (5%)

Homework

Lectures

DateTopic
Week 1, 1/16Cancelled due to weather conditions
Week 1, 1/18, Lecture 1Introduction to Computer Vision (slides)
Week 2, 1/23, Lecture 2Intensity Surface and Gradients (slides, code)
Week 2, 1/25, Lecture 3Image Filtering and Convolution (slides, code)
Week 3, 1/30, Lecture 4Smoothing (slides, code)
Week 3, 2/1Cancelled due to travelling
Week 4, 2/6, Lecture 5Edge Detection (slides)
Week 4, 2/8, Lecture 6Corner Detection (slides)
Week 5, 2/13, Lecture 7Laplacian and Blob Detection (slides)
Week 5, 2/15, Lecture 8Scale Invariance and SIFT I (slides)
Week 6, 2/20, Lecture 9Scale Invariance and SIFT II (slides, quiz)
Week 6, 2/22, Lecture 10Geometric Primitives and Transformations (slides)
Week 7, 2/27, Lecture 11Camera Projection (slides)
Week 7, 2/29, Lecture 12Camera Calibration (slides)
Week 8, 3/5, Lecture 13Epipolar Geometry (slides)
Week 8, 3/7Midterm Exam
Week 9, 3/12Spring Break
Week 9, 3/14Spring Break
Week 10, 3/19, Lecture 14Epipolar Geometry and Stereo (slides)
Week 10, 3/21, Lecture 15Structure from Motion I (slides)
Week 11, 3/26, Lecture 16Structure from Motion II (slides)
Week 11, 3/28, Lecture 17Convolution Neural Networks I (slides)
Week 12, 4/2, Lecture 18Convolution Neural Networks II (slides)
Week 12, 4/4, Lecture 19Convolution Neural Networks III (slides)
Week 13, 4/9, Lecture 20Recurrent Neural Networks I (slides)
Week 13, 4/11, Lecture 21Recurrent Neural Networks II (slides)
Week 14, 4/16, Lecture 22Transformer I (slides)
Week, 14, 4/18, Lecture 23Transformer II (slides)
Week 15, 4/23, Lecture 24Object Detection I (slides)
Week 15, 4/25, Lecture 25Object Detection II (slides, quiz)
Week 16, 4/30, Lecture 26Semantic Segmentation (slides)
Week 16, 5/2Final Exam

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