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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>CSE152A: Introduction to Computer Vision</title>
<link rel="stylesheet" type="text/css" href="cs468.css">
</head>
<body>
<p>DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING</br>UNIVERSITY OF CALIFORNIA, SAN DIEGO</p>
<h1 align="center">
<font color="#800000">CSE152A: Introduction to Computer Vision</font>
</h1>
<p></p>
<h2 align="center">
<font color="#800000">Winter 2022</font>
</h2>
<p></p>
<br>
<div id="content">
<div class="underlinemenu">
<ul>
<li><a href="index.html">General Info</a></li>
<li><a href="resources.html">Resources</a></li>
<li><a href="schedule.html">Schedule & Assignments</a></li>
<li><a href="https://piazza.com/class/kxxkt8n1m205nd">Piazza</a></li>
</ul>
</div>
<div class="underlinemenu"> </div>
<a name="announcements"></a>
<h3>Announcements</h3>
<ul>
<li>Welcome to the course! </li>
<li>Please sign up with <a href="https://piazza.com/class/kxxkt8n1m205nd">Piazza</a></li>
</ul>
<br>
<br>
<a name="info"></a>
<hr>
<h3>General Information</h3>
<strong>Times & Places</strong>
<br>Lecture: WeFr 5:00PM - 6:20PM, Zoom, <a href="https://ucsd.zoom.us/j/94341953328">Zoom</a>
<br>Discussion: 8:00pm-9:00pm, Thu<br><br>
<a name="staff"></a>
<strong>Course Staff</strong>
<table id="hor-minimalist-a" summary="Course Staff">
<thead>
<tr>
<th scope="col"><br>
</th>
<th scope="col">Name</th>
<th scope="col">Email</th>
<th scope="col">Office Hours</th>
<th scope="col">Location</th>
</tr>
</thead>
<tbody>
<tr>
<td>Instructor</td>
<td>Prof. Hao Su</td>
<td>[email protected]</td>
<td>1:00pm-2:00pm, Wed</td>
<td><a href="https://ucsd.zoom.us/my/haosuzoom">link</a></td>
</tr>
<!--
<tr>
<td>Instructor</td>
<td>Prof. Manmohan Chandraker</td>
<td>[email protected]</td>
<td>TBD</td>
<td></td>
</tr>
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<tr>
<td>Course Assistant</td>
<td>Yuying Yeh</td>
<td>[email protected]</td>
<td>10:00am-11:00am, Wed</td>
<td><a href="https://ucsd.zoom.us/j/4635172734">link</a></td>
</tr>
<tr>
<td>Course Assistant</td>
<td>Rui Zhu</td>
<td>[email protected]</td>
<td>2:00pm-3:00pm, Tue</td>
<td><a href="https://ucsd.zoom.us/j/95813795644">link</a></td>
</tr>
<tr>
<td>Course Assistant</td>
<td>Kunal Gupa</td>
<td>[email protected]</td>
<td>9:00am-10:00am, Fri</td>
<td><a href="https://ucsd.zoom.us/j/6282427103">link</a></td>
</tr>
<tr>
<td>Course Assistant</td>
<td>Tarun Kalluri</td>
<td>[email protected] </td>
<td>3:30pm-4:30pm, Mon</td>
<td><a href="https://ucsd.zoom.us/my/tarunkal">link</a></td>
</tr>
</tbody>
</table>
<h4>Topics</h4>
<ul>
<li>Camera Model</li>
<li>Multi-View Geometry</li>
<li>Structure from Motion</li>
<li>Optical Flow</li>
<li>Image Classification</li>
<li>Basic Convolutional Neural Network</li>
</ul>
<h4>Objectives</h4>
<p>The goal of computer vision is to compute properties of the three-dimensional world from images and video. Problems in this field include identifying the 3D shape of a scene, determining how things are moving, and recognizing familiar people and objects. This course provides an introduction to computer vision, including such topics as 3D shape reconstruction through stereo, motion estimation, and image classification. To reflect the latest progress of computer vision, we also include a brief introduction to the philosophy and basic techniques of deep learning methods.</p>
<p>Prerequisites: Linear algebra and calculus; data structures/algorithms; and Python or other programming experience.</p>
<p>Programming aspects of the assignments will be completed using Python.</p>
<p>Academic Integrity Policy: Integrity of scholarship is essential for an academic community. The University expects that both faculty and students will honor this principle and in so doing protect the validity of University intellectual work. In this class, we encourage students to form groups of two and work together on homeworks. This means that all academic work will be done by the pair of individuals to whom it is assigned, without unauthorized aid of any kind.</p>
<p>Collaboration Policy: It is expected that you complete your academic assignments in your own words (more specifically, for any write-up assignment each individual must submit an independent copy). For coding tasks, each individual must write your own copy. The assignments have been developed by the instructor to facilitate your learning and to provide a method for fairly evaluating your knowledge and abilities (not the knowledge and abilities of others). So, to facilitate learning, you are authorized to discuss assignments with others (even if he/she is not your team member); however, to ensure fair evaluations, you are not authorized to use the answers developed by another, copy the work completed by others in the past or present, or write your academic assignments in collaboration with another person.</p>
<p>If the work you submit is determined to be violating the rules, you will be reported to the Academic Integrity Office for violating UCSD's Policy on Integrity of Scholarship. In accordance with the CSE department academic integrity guidelines, students found committing an academic integrity violation will receive an F in the course.</p>
<p>Late Policy: No late day is allowed. However, <b>you can drop one out of nine assignments without penalty</b>. </p>
<h4>Homework, Exams, and Grading (tentative)</h4>
<ul>
<li>Weekly Homeworks: 80%</li>
<!--<li>The due dates of homeworks are at 11:59<b>AM</b> of Tue in the 4, 6, 8, and 10 weeks.</li>-->
<!--<li>In-class exam 5%</li>-->
<!--<li>Mid-term 20%, in the 5th week</li>-->
<li>Final Project: 20%</li>
<li>No In-class Exams</li>
</ul>
<br>
<hr>
<!--
<h4>Acknowledgements</h4>
Many of the lectures are based on the lecture slides from:
<ul>
<li><a href="http://graphics.stanford.edu/courses/cs468-17-spring/">CS468: Machine Learning on 3D Data</a></li>
<li><a href="http://cs233.stanford.edu/">CS233: Geometrical and Topological Data Analysis</a></li>
<li><a href="http://cs231n.stanford.edu/">CS231n: Convolutional Neural Networks for Visual Recognition</a></li>
<li><a href="http://people.cs.umass.edu/%7Ekalo/datadrivenshape/">Data Driven Shape Analysis and Processing</a></li>
</ul>
We would like to thank all authors for sharing their resources.
-->
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