Prelim 1	Monday October 3	9:40 – 10:55
Prelim 2	Wednesday November 2	9:40 – 10:55
Prelim 3	Monday December 5	9:40 – 10:55

Note: There will be a prelim on the last day of classes

Prelim 1	18%
Prelim 2	18%
Prelim 3	18%
Quizzes	16%
Labs	30%

There are three in-class exams for this course. Exams cover the material given in both lectures and labs. The second exam will focus on material that is not covered in the first exam.

Homeworks may be occasionally given based on the lecture material to reinforce the understanding of various course topics.

Computer labs assignments generally occur once a week during the first part of the course and are due after a week. The schedule for the labs is provided on the course website; this schedule may be revised during the semester. Lab assignments may either be conducted on ECE computer facilities or on the student’s personal computer. The final lab may have a project component.

Course assignments are due to be submitted to Canvas by 11:59 p.m. on the due date. Late assignments will receive a 10% grade penalty per day late. There are a total of three slip days allowed per semester for lab or homework assignments.

In general, slides presented in class will be made available on the course website; and a video recording of the class will be made available on canvas. These slides are not intended to be complete notes and students are responsible for taking notes on information presented in class that is not in the handouts.

Students are expected to attend class and to be there at the start of class. Pop-quizzes for credit may be given at any time.

Computer Vision is the construction of explicit meaningful descriptions of physical objects and other observable phenomena from images. Basic techniques for image processing and feature extraction are covered in lectures; topics include: image formation, edge detection, region growing, shape description, and machine learning for computer vision. Higher-level more experimental image analysis techniques, such as video image sequence analysis, are covered by selected presentations.

During the first part of the semester a sequence of computer labs will provide experience in the software tools that are important for computer vision applications and will include the basics of image processing, feature extraction and machine learning. In the last part of the semester, students will do additional labs on convolution neural networks and deep learning.

Essentially, ECE5470 is a first course on learning about how computers can see; that is, interpret the multidimensional signals provided by imaging sensors. Computer vision has a very wide range of applications from medical diagnosis to seeing robots, from particle physics to geological surveying. The objective of this course is to provide students with an understanding of the fundamental methods and an appreciation for the state of the art and the potential of computer vision.

Students will gain an understanding of the fundamental issues and techniques for extracting information from digital imagery. They will have a good knowledge of well-established methods for decomposing an image into basic elements: edges, regions, and other features. They will also be introduced to convolution neural networks and deep learning higher-level image understanding methods.

Introductory labs provide the student with experience in using computer systems and the associated specialized software tools for processing and extracting information from digital images.

The schedule and procedures in this course are subject to change as always.

Students expected to abide by the Cornell University Code of Academic Integrity with work submitted for credit representing the student’s own work. Discussion and collaboration on homework and laboratory assignments is permitted and encouraged, but final work should represent the student’s own understanding. Specific examples of this policy implementation will be discussed in class. Course materials posted on the computer vision website are for class use; students are not permitted to buy or sell any course materials without the express permission of the instructor. Such unauthorized behavior will constitute academic misconduct.

Masks are encouraged but not required in classrooms for Fall 22, according to university policy. However, the University strongly endorses compliance with requests to mask from students, staff, or faculty who are health compromised. If you are health compromised and would like me to request that the class be masked, please send me an email with any rationale that you are comfortable with me sharing. I do not need to identify you unless you would prefer that I do.

COVID 19 Symptoms

Zoom recordings of lecturers are not available for absences, including absences due to illness. For any illness, you are expected to keep up with course material by working with a peer in the course or accessing our shared notes page of CANVAS. We will allow time to find a peer buddy in the first week of the course.

If you have symptoms of COVID19, here is what you should do for my class:

1. Do not come to class if you have symptoms

2. Email me before class starts to let me know that you are not coming and are instead seeking out an antigen test. I expect that, according to university policy and free access to tests, you will be able to take a test immediately upon noticing symptoms and will have a result within minutes.

3. If the test is negative, I expect you to be in the next class, having caught up on material from a classmate or the shared notes page.

4. If your antigen test is positive, you must immediately upload the result to Daily Check. This will trigger instructions and a letter of temporary accommodation. You must forward this email to [me, TA] to receive accommodation, the system does not send it for you. Once you forward [me, TA] the letter, we will provide guidance on how you should keep up with material for the next 5 days (usually no more than one additional class will be missed).

5. Please wear a mask in class through the 10th day after symptoms begin.