Introduction to Computer Vision, Fall 2021

Course number: CSE 527

Meets: Tuesday/Thursday 6:30-7:50pm

Location: Light Engineering Lab 102 (in-person) + Zoom (online)

Website: http://michaelryoo.com/course/cse527/ 

Instructor: Michael S. Ryoo

Email: mryoo "at" cs.stonybrook.edu

Office: NCS 231

Office hours: Thursdays 10-11am, virtually

Course description:

Computer Vision is the study of enabling machines to "see" the visual world (i.e., understand images and videos). In this course, the students will learn fundamental computer vision algorithms and have opportunities to implement them. Further, we will be discussing more recent state-of-the-art visual representation learning approaches.

The topics to cover include:

Textbooks:

Computer Vision: Algorithms and Applications by Richard Szeliski

Computer Vision: A Modern Approach by David Forsyth and Jean Ponce

Prerequisites:

Prerequisites include a foundation in Linear Algebra and Calculus, and the ability to program. We will be programming in Python (OpenCV, NumPy, SciKit).

Schedule:

Week

Topic

Slides

Week1

Introduction

Recognition overview

ppt

ppt

Week2

Images and filters

Gradients

ppt

ppt

Week3

Edges

Programming assignment 1

ppt

Week4

Texture

Segmentation

ppt

ppt

Week5

Week6

Week7

Week8

Midterm (tentative)

Week9

Week10

Week11

Week12

Week13

Week14

Thanksgiving week

Week15

Week16

Final project presentations

Course requirements and grading:

Programming assignments (45%): 4~5 homeworks

Final project (25%): team project with presentations and reports

Midterm exam (10%)

Final exam (20%)

Acknowledgement:

This course has been inspired by the Computer Vision course by Kristen Grauman (UT), Devi Parikh (Gatech), Yong Jae Lee (UC Davis), and Dimitris Samaras (Stony Brook)