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<h2>Fall 22 - Introduction to Computer Vision</h2>
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<div class="trend-contents">
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<b style="color:rgb(68, 68, 68); font-size:22px"></b>
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<p><div class="column_1">Course title</div> <div class="column_2">EE 379K: Introduction to Computer Vision</div></p>
<p><div class="column_1">Term</div>
<div class="column_2">Fall 2022</div></p>
<p><div class="column_1">Meeting times and location</div>
<div class="column_2"> TR 5:00-6:30pm (ETC 2.136) </div></p>
<p><div class="column_1">After-class platform</div>
<div class="column_2"> Slack (link sent to registered students) </div></p>
<p><div class="column_1">Video recording</div>
<div class="column_2"> No (fully in-person) </div></p>
</div>
</div>
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<b style="color:rgb(68, 68, 68); font-size:22px">Course Description and Prerequisites</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<p>Computer vision (CV) is the discipline of “teaching machines how to see”: it makes sense of photographs, video, and other imagery. Applications include analysis of medical images, automated quality inspection, entertainment, vehicle safety, security, and HCI, among many others. This course offers a gentle introduction to computer vision, including image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. Both classical and the latest deep learning approaches will be covered.</p>
<p>The students will digest and practice their knowledge and skills by both homework and a midterm exam. They will also obtain in-depth experience with a particular topic through a final project. There will be no final exam.</p>
<p>Students should have taken the following courses or equivalent: Algorithms (EE 360C or CS 314/314H), Linear Systems and Signals (EE313 or BME 343), Probability and Random Processes (EE 351K or BME 335 or MATH 362K). <b style="color:rgb(71, 71, 71)">Solid Knowledge of Linear Algebra will be instrumental to this course</b>. </p>
<p>Coding experiences with Python are assumed. Previous knowledge of C/C++, MATLAB, or PyTorch/Tensorflow is very helpful, but not necessary.</p>
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<b style="color:rgb(68, 68, 68); font-size:22px; padding-bottom:50px">Instructor Information</b>
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<p><div class="column_1">Name</div> <div class="column_2">Dr. Zhangyang (Atlas) Wang</div></p>
<p><div class="column_1">Telephone number</div> <div class="column_2">512-471-1866</div></p>
<p><div class="column_1">Email address</div>
<div class="column_2"><a herf="mailto:[email protected]">[email protected]</a></div></p>
<p><div class="column_1">Office hour time</div>
<div class="column_2">Wednesday 10:00am - 11:00am </div></p>
<p><div class="column_1">Office hour location</div>
<div class="column_2">EER 6.886 (instructor office)</div></p>
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<b style="color:rgb(68, 68, 68); font-size:22px; padding-bottom:1px">TA Information</b>
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<p><div class="column_1">TA 1 Name</div> <div class="column_2"><a href="https://ir1d.github.io/">Dejia Xu</a></div></p>
<p><div class="column_1">Email address</div>
<div class="column_2"><a herf="mailto:[email protected]">[email protected]</a></div></p>
<p><div class="column_1">Office hour time</div>
<div class="column_2">Monday 4:00-5:00pm </div></p>
<p><div class="column_1">Office hour location</div>
<div class="column_2">outside EER O’s Campus Café, outdoor seating area </div></p>
</div>
</div>
<div style="padding-bottom:50px" class="trend-contents">
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<p><div class="column_1">TA 2 Name</div> <div class="column_2"><a href="https://wenyancong.com/">Wenyan Cong</a></div></p>
<p><div class="column_1">Email address</div>
<div class="column_2"><a herf="mailto:[email protected]">[email protected]</a></div></p>
<p><div class="column_1">Office hour time</div>
<div class="column_2">Friday 4:00-5:00pm </div></p>
<p><div class="column_1">Office hour location</div>
<div class="column_2">outside EER O’s Campus Café, outdoor seating area </div></p>
</div>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Textbook and/or Resource Material</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<p>This course does not follow any textbook closely. Among many recommended readings are:</p>
<ul>
<li><a href="http://szeliski.org/Book/">Computer Vision: Algorithms and Applications</a>, Richard Szeliski (2010). 【<b style="color:rgb(71, 71, 71)">Most Recommended for CV beginners</b>】</li>
<li><a href="https://www.youtube.com/channel/UCf0WB91t8Ky6AuYcQV0CcLw">First Principles of Computer Vision (YouTube Lecture)</a>, Shree Nayar (2021). 【<b style="color:rgb(71, 71, 71)">Classical CV topics, especially non-ML</b>】</li>
<li><a href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf">Pattern Recognition and Machine Learning</a>, Christopher M. Bishop (2006).【<b style="color:rgb(71, 71, 71)">Classical ML</b>】</li>
<li><a href="http://www.deeplearningbook.org/">Deep Learning</a>, Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016).</li>
<li><a href="https://d2l.ai/">Diving into Deep Learning</a>, Aston Zhang, Zack Lipton, Mu Li and Alex Smola (2019).</li>
</ul>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Grading Policies</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<P>Grading will be based on homework (<B>20%</B>; there will be 4 assignments), one mid-term exam (<B>30% - time/format TBD</B>), and one final project (<B>50%</B>) (proposal <B>10%</B> + presentation <B>10%</B> + final report <B>20%</B> + code review <B>10%</B>).</P>
<ul>
<li>One project to receive the Best Project Award, voted by all class members. (<b>+5%</b>)</li>
<li>Projects in the novel, interdisciplinary domains (some examples: 5G/6G telecommunication, brain-computer interface, economics & markets, COVID-19, etc.), judged by the instructor. (<b>+2%</b>)</li>
<li>For late submission, each additional late day will incur a <b>10%</b> penalty.</li>
<li> Request for re-grading an assignment must be made in writing within one (1) week of the graded assignment being made available to the class.</li>
</ul>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Course Topics</b>
<div style="color:rgb(68, 68, 68)" class="trend-contents">
<br />
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/23 Tuesday</div> <div class="column_2">Class Logistics, and Fundemental Vision Theory <a href="Fall22/Lecture 1.pdf">[Slides 8/23]</a>
<br> <em>(Extended Materials: <a href="https://www.youtube.com/watch?v=Di_3pGAveGs&t=944s">MIT lecture on "Marr’s Level’s of Analysis"</a>)</em></div></p>
</div>
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<p><div class="column_1">8/25 Thursday</div> <div class="column_2">Image Representation (1): From Our Brain to the Digital World </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/30 Tuesday</div> <div class="column_2">Image Representation (2): Gaussian and Laplacian Image Pyramids </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/01 Thursday</div> <div class="column_2">TA Lecture: Q&A on Course Projects & Cracking the Coding! <a href="Fall22/0901_project.pdf">[Slides 9/01]</a> <a href="Fall22/code_basis.ipynb">[Jupyter Notebook]</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/06 Tuesday</div><div class="column_2">Image Representation (3): Taking A Frequency Domain View <a href="Fall22/Lecture 2+3+4.pdf">[Slides 8/25 + 8/30 + 9/06]</a> <br> <em>(Extended Materials: <a href="http://users.ece.utexas.edu/~bevans/courses/signals/lectures/05_Sampling_Aliasing/index.html">Review of Sampling, Aliasing, and Fourier Analysis Methods</a>)</em> </div></p>
</div>
<br>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/08 Thursday</div><div class="column_2">Image Filtering (1): Pointwise, Convolution, and Beyond <a href="Fall22/Lecture 5.pdf">[Slides 9/08] </a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/13 Tuesday</div> <div class="column_2">Image Filtering (2): Edge Detection, from Sober to Canny <a href="Fall22/Lecture 6.pdf">[Slides 9/13] </a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/15 Thursday</div> <div class="column_2"> Cross-Image Matching (1): Detecting Key Points </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/20 Tuesday</div> <div class="column_2"> Cross-Image Matching (2): Extracting Feature Descriptors from Key Points </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/22 Thursday</div> <div class="column_2"> Cross-Image Matching (3): Robust Matching of Descriptors <a href="Fall22/Lecture 7+8+9.pdf">[Slides 9/15 + 9/20 + 9/22]</a> <br> <em>(Extended Materials: <a href="https://www.deeplearningbook.org/contents/linear_algebra.html">Review of Linear Algebra, especially EVD, SVD and PCA</a>)</em> </div></p>
</div>
<br>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/27 Tuesday</div> <div class="column_2"> Mapping 3D World to Image (1): Pinhole and Lens Cameras </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/29 Thursday</div> <div class="column_2"> Mapping 3D World to Image (2): Developing the Pinhole Camera Model </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1"> 10/04 Tuesday</div> <div class="column_2"> Mapping 3D World to Image (3): Geometric Camera Calibration <a href="Fall22/Lecture 10+11+12.pdf">[Slides 9/27 + 9/29 + 10/04]</a> <br> <em>(Extended Materials i: <a href="https://www2.math.uconn.edu/~leykekhman/courses/MATH3795/Lectures/Lecture_9_Linear_least_squares_SVD.pdf">Solving Least Sqaures using SVD</a>)</em> <br> <em>(Extended Materials ii: <a href="https://docs.opencv.org/4.5.3/d7/d53/tutorial_py_pose.html">Geometric Camera Calibration in Action: An OpenCV Example</a>) </em></div></p>
</div>
<br>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/06 Thursday</div> <div class="column_2"> Stereo Vision (1): Two-Camera Models, and Triangulation </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/11 Tuesday</div> <div class="column_2">Stereo Vision (2): Epipolar Geometry</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/13 Thursday</div> <div class="column_2"> Stereo Vision (3): Essential and Fundemental Matrices </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/18 Tuesday</div> <div class="column_2"> Stereo Vision (4): Depth Estimation <a href="Fall22/Lecture 12+13+14.pdf">[Slides 10/06 + 10/11 + 10/13 + 10/18]</a> </div></p>
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<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/20 Thursday</div> <div class="column_2"> Video and Optical Flow (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/25 Tuesday</div> <div class="column_2"> Video and Optical Flow (2) <a href="Fall22/Lecture 15.pdf">[Slides 10/20 + 10/25]</a> </div></p>
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<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/27 Thursday</div> <div class="column_2"> Classical Machine Learning (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/01 Tuesday</div> <div class="column_2"> Classical Machine Learning (2) <a href="Fall22/Lecture 16+17.pdf">[Slides 10/27 + 11/01]</a> </b> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/03 Thursday</div> <div class="column_2"> Image Classification: Bag-of-Words <a href="Fall22/Lecture 18.pdf">[Slides 11/03]</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/08 Tuesday</div> <div class="column_2"> Object Detection and Segmentation (1) <br> <em>(Extended Materials: <a href="https://towardsdatascience.com/the-intuition-behind-facial-detection-the-viola-jones-algorithm-29d9106b6999">The Viola-Jones Algorithm Explained in Details</a>)</em> </div></p>
</div>
<br>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/10 Thursday</div> <div class="column_2"> Object Detection and Segmentation (2) <a href="Fall22/Lecture 19+20.pdf">[Slides 11/08 + 11/10]</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/15 Tuesday</div> <div class="column_2"> Deep Learning in Computer Vision (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/17 Thursday</div> <div class="column_2"> Deep Learning in Computer Vision (2) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/22 Thursday</div> <div class="column_2"> - No Class (Thanksgiving Break) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/24 Thursday</div> <div class="column_2"> - No Class (Thanksgiving Break) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/29 Tuesday</div> <div class="column_2">Deep Learning in Computer Vision (3) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">12/01 Thursday</div> <div class="column_2"> Deep Learning in Computer Vision (4) <a href="Fall22/Lecture 21-24.pdf">[Slides 11/15 + 11/17 + 11/29 + 12/01]</a> </div></p>
</div>
</div>
</div>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Acknowledgement</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<P>Many materials included in this course are adapted from the existing teaching or tutorial slides, created by colleagues in CMU, Stanford, UIUC, UC Berkeley, GaTech, Brown, and more. The instructor owes many thanks for their generosity of sharing those materials publicly.</P>
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