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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

+

CSE152A: Introduction to Computer Vision

+

Fall 2018

+
diff --git a/index.html b/index.html new file mode 100644 index 0000000..521d7aa --- /dev/null +++ b/index.html @@ -0,0 +1,161 @@ + + + + + + CSE152A: Introduction to Computer Vision + + + + + +

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

+

+ CSE152A: Introduction to Computer Vision +

+

+

+ Winter 2022 +

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Announcements

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General Information

+ Times & Places +
Lecture: WeFr 5:00PM - 6:20PM, Zoom, Zoom +
Discussion: 8:00pm-9:00pm, Thu

+ + Course Staff + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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NameEmailOffice HoursLocation
InstructorProf. Hao Suhaosu@ucsd.edu1:00pm-2:00pm, Wedlink
Course AssistantYuying Yehyuyeh@eng.ucsd.edu10:00am-11:00am, Wedlink
Course AssistantRui Zhurzhu@eng.ucsd.edu2:00pm-3:00pm, Tuelink
Course AssistantKunal Gupak5gupta@eng.ucsd.edu9:00am-10:00am, Frilink
Course AssistantTarun Kallurisskallur@eng.ucsd.edu 3:30pm-4:30pm, Monlink
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Topics

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Objectives

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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.

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Prerequisites: Linear algebra and calculus; data structures/algorithms; and Python or other programming experience.

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Programming aspects of the assignments will be completed using Python.

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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.

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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.

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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.

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Late Policy: No late day is allowed. However, you can drop one out of nine assignments without penalty.

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Homework, Exams, and Grading (tentative)

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+ + + diff --git a/resources.html b/resources.html new file mode 100644 index 0000000..a323f18 --- /dev/null +++ b/resources.html @@ -0,0 +1,37 @@ + + + + CSE152A: Introduction to Computer Vision + + + + +

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

+

CSE152A: Introduction to Computer Vision

+

Winter 2022

+
+

Resources

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References

+ The book from Richard Szeliski (Second edition) is the main reference of course contents: http://szeliski.org/Book/. You can download the PDF of the full book from the book website. + + +

Getting Help

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The IDEA Engineering Student Center, located just off the lobby of Jacobs Hall, is a hub for student engagement, academic enrichment, personal/professional development, leadership, community involvement, and a respectful learning environment for all. The Center offers a variety of programs, listed in the IDEA Center Facebook page at http://www.facebook.com/ucsdidea/ (you are welcome to Like this page!) and the Center web site at http://idea.ucsd.edu/. The IDEA Center programs support both undergraduate students and graduate students.

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Diversity and Inclusion

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We are committed to fostering a learning environment for this course that supports a diversity of thoughts, perspectives and experiences, and respects your identities (including race, ethnicity, heritage, gender, sex, class, sexuality, religion, ability, age, educational background, etc.). Our goal is to create a diverse and inclusive learning environment where all students feel comfortable and can thrive.

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Our instructional staff will make a concerted effort to be welcoming and inclusive to the wide diversity of students in this course. If there is a way we can make you feel more included please let one of the course staff know, either in person, via email/discussion board, or even in a note under the door. Our learning about diverse perspectives and identities is an ongoing process, and we welcome your perspectives and input.

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We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community (https://ucsd.edu/about/principles.html). Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and feels comfortable.

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If you experience any sort of harassment or discrimination, please contact the instructor as soon as possible. If you prefer to speak with someone outside of the course, please contact the Office of Prevention of Harassment and Discrimination: https://ophd.ucsd.edu/.

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Students with Disabilities

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We aim to create an environment in which all students can succeed in this course. If you have a disability, please contact the Office for Students with Disability (OSD), which is located in University Center 202 behind Center Hall, to discuss appropriate accommodations right away. We will work to provide you with the accommodations you need, but you must first provide a current Authorization for Accommodation (AFA) letter issued by the OSD. You are required to present their AFA letters to Faculty (please make arrangements to contact me privately) and to the OSD Liaison in the department in advance so that accommodations may be arranged.

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+ + + diff --git a/schedule.html b/schedule.html new file mode 100644 index 0000000..113fead --- /dev/null +++ b/schedule.html @@ -0,0 +1,192 @@ + + + + + Schedule + + + + + + +

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

+

+ CSE152A: Introduction to Computer Vision +

+

+

+ Winter 2022 +

+

+
+ +
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+ +
+

Schedule

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Course Logistics

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1/5HTML PDFIntroduction, Course logistics, Background test
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Math for Vision

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1/7 1/12 1/14HTML PDFLinear Algebra Review
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Camera Model

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1/19HTML PDFIntrinsic Camera Matrices, Extrinsic Camera Matrices, 3D Rigid Transformation
1/21HTML PDFVanishing Point, Perspective Projection
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Multi-View Reconstruction

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1/26HTML PDFEstimating 3D from Correspondence, Gradient Descent, Epipolar Geometry, 8-point algorithm
1/28HTML PDFRobust Estimation of Fundamental Matrix, Lagrange Multiplier Method, Pre- and Post-processing, Low-rank Approximation
2/2HTML PDFAnalytical Solution of Fundamental Matrix, RANSAC
2/4HTML PDFMid-term Review
2/9HTML PDFDecode (R,t) from E, Corner Detection
2/11HTML PDFCorner Detection
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Tracking

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2/16HTML PDFoptical flow
2/18HTML PDFLucas-Kanade
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Recognition

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2/23HTML PDFdata-driven, nearest neighbor
2/25HTML PDFlogistic regression
3/2HTML PDFbasics of neural networks
3/4PDFdeep neural network, bias-variance tradeoff
3/9PDFmore of deep learning
3/11PDFconvolutional neural network
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+ + diff --git a/slides/ConvNet.key b/slides/ConvNet.key new file mode 100755 index 0000000..da7b308 Binary files /dev/null and b/slides/ConvNet.key differ diff --git a/slides/ConvNet.pdf b/slides/ConvNet.pdf new file mode 100644 index 0000000..bf109ab Binary files /dev/null and b/slides/ConvNet.pdf differ diff --git a/slides/NN_more.key b/slides/NN_more.key new file mode 100755 index 0000000..76d0694 Binary files /dev/null and b/slides/NN_more.key differ diff --git a/slides/NN_more.pdf b/slides/NN_more.pdf new file mode 100644 index 0000000..0847b23 Binary files /dev/null and b/slides/NN_more.pdf differ