Description
The objective of the final project is to make use of the algorithms and tools learned
during the semester, and to develop some creative new applications of computer
vision. You and your team will write a detailed report and will also demo the project
for your fellow classmates during the last week of classes.
You can work in groups of at most three students. We recommend you divide the
work, share ideas, algorithms, tools, and choose a collaborative development tool
such as GitHub in order to minimize the time needed to meet in person. (Note:
disregard advice about meeting in person if you and your teammates live in the
same residence). Because of the physical limitations, we understand that one
member of the group might not be fully aware of what a partner has implemented.
• The code/implementation part of the assignment can be one submission
from only one of the team members, with a clear note/comment of who the
team members are.
• The report can have some common parts, but it should be an individual
assignment, which will be turned in individually; there will be a separate
submission link on Canvas.
• Your final demo should showcase the contribution of each member of the
group.
You need to choose one of two project areas:
A. The Photo Sorter project
B. An interactive project of your own choice that includes either
• An object (or face) recognition/classification task, or
• An implementation of an algorithm from literature.
Grading will be based on the completeness of the project, the level of functionality
attempted, the clarity and thoroughness of the report, and quality of the demo.
Further specifics on grading are given for each project below.
Project Details – Option A. Photo Sorter
Over the last decade, digital cameras have overtaken traditional film cameras. As a
result, we are amassing ever larger consumer archives of photos as well as large
online repositories such as Instagram, Facebook, and Google Photos. Facebook is
currently the largest photo online storage site, with over 300 million photos
updated every day (300 million users use Facebook Stories, 500 million users use
Instagram Stories every day – source:https://zephoria.com/top-15-valuable-facebook-statistics/).
Digital cameras have tended to alter people’s photo-taking behavior. Since images
are now “free”, most people take more pictures. They also take many duplicates in
the hope of getting one “really good” shot. But, as these photos come off the camera,
they tend to pile up on the drive without any useful sorting or clustring. This is
where you come in.
The project’s objective is to develop several photo sorting tools. The project ground
rules are as follows:
• You are supplied with a folder of digital images.
https://drive.google.com/open?id=1YFxLAJx1V2tFRFQ4xOceLGIF9waBa-Zc
Your job is to detect common attributes among photos and use them to create
“clusters” of similar photos. Optionally, you can also apply labels to the clustered
photos or save them into subfolders.
• You will create an interactive application interface that can be demonstrated.
For example, you can present the user with a menu/choice driven application
with functionality such as:
o load the images set
o sort images
o select only:
§ outdoor scenes
§ portraits (of one or a group of people)
§ text documents
§ images containing cars
§ images containing sky
§ images containing flowers
§ images containing buildings, etc.
o add image to the set and re-sort, etc.
Note: these are only ideas and suggestions; you are free to come up with
your own functions and tasks.
• You can use the programming language of your choice.
There are three levels of grading for this project:
1. Baseline: The baseline project is to detect near-duplicate photos and to group
them. Near-duplicates may differ in small details, but the overall composition
would be the same. A project that does this well will get 75% credit.
2. Preferred: The preferred project will perform baseline operations, but also be
able to detect common portions of images and group images based on those.
Examples from the menu above are all valid choices. Your project could detect
that scenes contain the same people or objects (but might differ in composition)
and will group based on those objects. This does not need to be a generalpurpose functionality. That is, it suffices, for example, to recognize some small
set of people, and it is ok to assume the people have not altered their
appearance. A project that exhibits 2-3 of these additional capabilities would get
100% credit.
3. Deluxe: A deluxe project would add additional functionality, a polished user
interface, interactivity, or interfaces to other tools (Facebook, Flickr, Instagram
etc.). Example: implement interactive tools where, for example, the user can click
or circle a part of the image, and then use the properties of that image patch later
in your algorithm. Credit for deluxe items will be given on a case-by-case basis.
Project Details – Option B. Interactive project of your choice
Goals: Over the course of the semester we have become familiar with many tools
and techniques used in Computer Vision today. With this set of tools you can
actually solve some interesting and challenging problems. The final project is your
chance to apply some of these tools to a problem that interests you.
Important: If you are a graduate or undergraduate student and you are interested
in using machine learning or deep learning, please get approval for your project.
Your project must have a not-negligible “Classic Computer Vision” component.
Projects that focus solely in training a neural network will not be accepted.
There are a number of components to a good project:
• Choosing the project: It’s hard to choose something that is both interesting
and doable in 3.5 weeks. Ask for help with this process.
• Formalizing the problem: How do you take an idea like “detecting mouths in
a single image” and formalize it and then turning it into something that can
be computed. This is connected with the algorithm development part.
• Data collection: You will need data with which to test your algorithm. The
safest bet is to use data sets that others have already gathered and made
available and that have been used for a similar task to yours. Ask us if you
need help. It’s not always the best idea to collect new data because it can be
time consuming and increase the level of challenge for your project.
• Algorithm development: You can use the programming language of your
choice. Set incremental goals. Test your software in stages. Use good
programming practices (don’t let Matlab/Python make you lazy). Try to
break your code! If it works the first time don’t believe it; try strange/edge
cases.
• Experimentation: You want to show us experiments that illustrate the ideas
and the behavior of your method. Don’t overwhelm us with results but rather
choose good illustrative examples (of success and failure).
• Write-up: A key part of any research project is effective communication of
the results. Unlike the initial homework assignments in the course, your
grade will be determined in a big part by your write-up.
Tips: Don’t get carried away. Choose something that seems small and then set
intermediate goals. You want to have a fallback strategy if you don’t meet your
ultimate goals. These projects should not be basic research. If nobody has ever done
this before then the risks are too high for a final project. Choose something from the
literature that seems doable and implement that.
Note: the Web is a great source of ideas, papers, data, and even code. If you do get
data, code, or ideas from the web you MUST disclose this. Become a fanatic about
appropriately citing work from any source. Your write-up should specify in detail
your sources and your personal contribution to every task in your algorithm, and
every piece of your pipeline. If we find any portion of your submitted solution code
on a website, it means you have violated the Honor Code rules.
Suggestions for everyone:
• Start early: This assignment involves plenty of implementation and multiple
inter-connected components can make it hard to debug.
• Start with small data: Verify your system on small data before massive
processing.
• By aware of the running time: try to optimize your implementation using
vector-based operations and avoid to loop for every pixel.
Submission Guidelines (for all projects):
The Final report will represent 50% of your grade. Regardless of which project you
choose, you should document the following:
o Your initial project idea and project goals.
o Your algorithm/approach and your hypothesis (what will work, what do you
think you’ll be able to achieve).
o The data analysis and the results: which goals you managed to achieve.
o The computer vision methods you used to achieve these goals.
o If things did not work out, try to offer a reason for it.
o Known limitations of the methods you have used and possible future
extensions.
o Your contributions to the project. Because this part of the report should be
different for each team member, we expect individual report submissions
for each team member!
o List at least 3 things you learned during this project.
o What advice you would give to next year’s vision students? Don’t skip this part.
We are looking forward to reading your recommendations.
During the last week of classes each team is required to make a demo or
presentation of their project. Focus on what your goals were at the beginning, the
challenges encountered and your final results. We will create a scheduler on Canvas
for teams to present during the live lecture, using the Screen Sharing option in
Zoom. We are looking forward to finally meeting everyone.
For teams who are in the Distance section of the course: record and submit a
video/screencast, and drop your video file in this shared folder:
https://drive.google.com/drive/folders/1YGJmu0_w3fKlogoZU7k65wGDpLjg_88?usp=sharing
Note1: If your project will make use of a neural network (CNN or otherwise), it is
not sufficient to present just your results. You need to argument your choice of
parameters for your network and explain how the training was done. Training from
scratch won’t work very well and otherwise you’ll have to fine tune a pre-trained
existing network. Keep in mind the purpose of this project is to put together
everything you learned this semester in order to build a computer vision application
pipeline.
Note2: If you are combining your course project with a project from another class,
you must receive permission from both instructors, and clearly explain in the report
the exact portion of the project that is being counted for CSCI 4831/5722. In this
case you must prepare separate reports for each course and submit your final
report for the other course as well.
The submission for each student should include:
1. Solution code, as a zip file, via Canvas before Tuesday, April 20th, by 10:00pm
MDT
2. Written report, as a pdf file, via Canvas before Friday, April 23rd, by 10:00pm
MDT
3. (If in the Distance section) A video, via Google Drive folder before Sunday,
April 25th, by 11:55pm MDT
