CS 231A Course Project



Course Project Overview

You need to propose an original research topic, or replicate an existing paper. Need instructor's approval.
• Project Ideas and Suggestions
Project Reports of Previous Years


Important Dates

Oct 21 (11:59pm), Finalizing team members : Maximum team size: 2. Send us an email with your team name and team members.


Oct 21 (11:59pm), Proposal submission : Submit a 0.5 page course project proposal in our provided template. Send a PDF file to cs231asubmit@gmail.com


Nov 18 (11:59pm), Project milestone : Submit a 2-3 page course project milestone report.


Dec 13 (11:59pm), Final report and code submission : No late days allowed.


Dec 14 (14:00pm), Project presentation submission : No late days allowed.


Dec 15 (10am - 12pm), Course project presentation. Location: Room 200-305 (Room 305 of Building 200)



Grading Policy

Final Project: 40%

presentation: 5%
write-up: 10%
 •  clarity, structure, language, references: 3%
 •  background literature survey, good understanding of the problem: 3%
 •  good insights and discussions of methodology, analysis, results, etc.: 4%
technical: 15%
 •  correctness: 5%
 •  depth: 5%
 •  innovation: 5%
evaluation and results: 10%
 •  sound evaluation metric: 3%
 •  thoroughness in analysis and experimentation: 3%
 •  results and performance: 4%

Project Submission Details

   Write-up and Code submission

You must use our provided templates. Email your project proposal, milestone report, final report and zipped code to: cs231asubmit@gmail.com , with the following format:

Subject Line: Course Project Proposal/Milestone/Report
Body: Full names of all group members, SUNet ID's and Project title
Attachments: Write-up as LastName_LastName_Paper.pdf, Code as LastName_LastName_Code.zip, where the titles have all the last names of the group members.

  Presentation submission

You must use our provided presentation template. Email your Powerpoint slides (.ppt file) to cs231asubmit@gmail.com, with the following format:

Subject Line: Course Project Presentation
Body: Full names of all group members, SUNet ID's and Project title
Attachments: Powerpoint slides as LastName_LastName_Presentation.ppt, where the titles have all the last names of the group members.


Final Report Write-up Guidelines

Your final write-up should be between 8 - 10 pages using the template provided. After the class, we will post all the final reports online (restricted to CS231a students only) so that you can read about each others’ work. If you do not want your writeup to be posted online, then please let us know at least a week in advance of the final writeup submission deadline. The following is a suggested structure for your report:


• Title, Author(s)

• Abstract: It should not be more than 300 words;

• Introduction: this section introduces your problem, and the overall plan for approaching your problem

• Background/Related Work: This section discusses relevant literature for your project

• Approach: This section details the framework of your project. Be specific, which means you might want to include equations, figures, plots, etc

• Experiment: This section begins with what kind of experiments you're doing, what kind of dataset(s) you're using, and what is the way you measure or evaluate your results. It then shows in details the results of your experiments. By details, I mean both quantitative evaluations (show numbers, figures, tables, etc) as well as qualitative results (show images, example results, etc).

• Conclusion: What have you learned? Suggest future ideas.

• References: This is absolutely necessary. Reports without references will not receive a score higher than 20 points (total is 40 points).

• Supplementary materials: This is NOT counted toward your 8-10 page limit. Please submit your codes as supplementary materials.


Project Presentation Guidelines

Each team should give a two minutes project presentation. After your presentation, there will be one minute for audiences to ask questions. You must use the template provided by us (Download it here) and make sure that your presentation contains exactly two slides. We will compile the presentation slides from all teams into a single big .ppt file and show it using our laptop, so you do not need to worry about bringing computers on the presentation day.


Project Proposal

We have provided the template for your final write-up. Your proposal should follow the same template, and should be no more than 1 page. Your proposal should describe as clearly as possible the following:


• What is the computer vision problem that you will be investigating? Why is it interesting?

• What image or video data will you use? If you are collecting new datasets, how do you plan to collect them?

• What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?

• Which reading will you examine to provide context and background?

• How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?


Project Milestone

Your project milestone report should be between 2 - 3 pages using the template provided. The following is a suggested structure for your report:

• Title, Author(s)

• Introduction: this section introduces your problem, and the overall plan for approaching your problem

• Problem statement: Describe your problem precisely specifying the dataset to be used, expected results and evaluation

• Technical Approach: Describe the methods you intend to apply to solve the given problem

• Intermediate/Preliminary Results: State and evaluate your results upto the milestone



Honor Code

You may consult any papers, books, online references, or publicly available implementations (such as SIFT) for ideas and code that you may want to incorporate into your strategy or algorithm, so long as you clearly cite your sources in your code and your writeup. However, under no circumstances may you look at another group’s code or incorporate their code into your project.


Project Reports of Previous Years

Winter, 2010-2011

3D Model Segmentation and Labeling

Generic Object/Scene recognition for the Smart Album Project

Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning

Learning Slow Features for Object Recognition

RoboGrader: Scoring Multiple Choice Tests with a Smartphone

Joint Subclassing and Classification

Heuristics for Decision Tree Selection and Weight Assignment in Random Forest for Fine-Grained Image Classification

Hole Filling Method Using Edge Based Interpolated Depth for View Synthesis

RGB-Z Segmentation of Objects in a Cluttered Scene Using a Kinect Sensor

Simultaneous Segmentation and Tracking in Point Cloud Data using the Iterative Closest Point Algorithm

KFace3D: Facial Recognition using RGBD Data

Comparison of Aircraft Tracking Using Top-Down and Bottom-Up Approaches

Geometric Understanding of Indoor Scenes

Object Pose Estimation using Optical Flow and POSIT

Real Time Subcutaneous Vein Recognition of Forearm Veins

Face Detection and Tracking for BabyCam

Image Retrieval, Semantic & Geographic Annotation using visual/multimedia representations and textual information

Smart Album: Face Recognition and Landmark Recognition in Album

Computer-assisted Detection of Defects during the Fabrication of PDMS chips

Unsupervised Learning of Invariances with Temporal Coherence

Image-based Web Page Classification


Winter, 2009-2010

Using a Functionality Model for Chair Detection

Fusing Multi-Channel Cues for Image Organization

Generalizing ImageNet to SmartPhones

Motion-sensitive Low-noise Imaging

Unsupervised Image Segmentation using Deep Belief Nets

The Retinal Algorithm to Detect, Segment and Track Moving Objects with Observer Motion

Unsupervised Feature Learning of Bi-modal Features

Efficient Classification and Segmentation of Specular Objects

Feature Descriptors for Tiny Image Categorization

A feature tracking approach to painted aperture

Baseline Scene Classifications

Camera Tracking with Fixed Point Math for Mobile Devices

Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities

Sub-meter Indoor Localization in Unmodified Environments with Inexpensive Sensors

Segmentation of seismic images

Object Detecting in Images using Time Series Ensemble Methods

Learning Visual Invariance in a 2-Layer Neural Network