homework computer vision

This page contains links to programming assignments. Reference material is available on the Lectures page.

(Due Sep 23rd)

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(Due Dec 2nd)

(Due Dec 11th)

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Assignment 1: Analyzing Parallel Program Performance on an Eight-Core CPU

Assignment 2: A Simple CUDA Renderer

Assignment 3: Processing Big Graphs on the Xeon Phi

Assignment 4: A Simple, Parallel Webserver

Administrivia

Instructor:	([email protected], please use "16-720" as subject)
TAs:	Achal Dave ([email protected])
	Sashank Jujjavarapu ([email protected])
	Siddarth Malreddy ([email protected])
	Brian Pugh ([email protected])
Piazza link:
Gradescope link:	(use entry code MNNZ8M)
Lectures:	Tues,Thur	12:00-1:20pm	Doherty Hall	1212
TA office hours:	Mon-Thurs	5:00-6:30pm	Smith Hall	200
Deva's office hours:	Tues	1:30-3:00pm	Smith Hall	221

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Instructor: Office location: Office hours: Tues 2:30-3:30 pm and by appointment

TA: Office location: TA Station #4, GDC Office hours: Office location: TA Stations Office hours: Mon 4:30-5:30 pm and Fri 3-4 pm Office location: TA Station #4 GDC 1.302 Office hours: Tues/Thurs 5-6 pm Please use for assignment help.

I. Features and filters: low-level vision Linear filters Edges and contours Binary image analysis Background subtraction Texture Motion and optical flow II. Grouping and fitting: mid-level vision Segmentation and clustering algorithms Hough transform Fitting lines and curves Robust fitting, RANSAC Deformable contours Interactive segmentation III. Multiple views Local invariant feature detection and description Image transformations and alignment Planar homography Epipolar geometry and stereo Object instance recognition IV.  Recognition: high-level vision Object/scene/activity categorization Object detection Supervised classification algorithms Probabilistic models for sequence data Visual attributes Active learning Dimensionality reduction Non-parametric methods and big data Deep learning, convolutional neural networks Other advanced topics as time permits

Textbook The course textbook is:     Computer Vision: Algorithms and Applications, by Rick Szeliski . It is freely available online or may be purchased in hardcopy.  Course lecture slides will be posted below and are also a useful reference. You may also find the following books useful. Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce Computer Vision, Linda G. Shapiro and George C. Stockman Introductory Techniques for 3-D Computer Vision, Emanuele Trucco and Alessandro Verri. Multiple View Geometry in Computer Vision, Richard Hartley and Andrew Zisserman. Pattern classification, Richard O. Duda, Peter E. Hart, and David G. Stork Pattern Recognition and Machine Learning.  Christopher M. Bishop Visual Object Recognition .  K. Grauman and B. Leibe

	Date	Topic	Readings and links	Lectures	Assignments
	Thurs Jan 18	Course intro	Textbook Sec 1.1-1.3
	Tues Jan 23
	Tues Jan 30
	Thurs Feb 1		Sec 10.5
	Tues Feb 6		Sec 8.4 (up until 8.4.1)
	Thurs Feb 8		Sec 4.3.2
	Tues Feb 13
			Sec 5.1.1
	Tues Feb 20		Sec 5.2-5.4
	Thurs Feb 22				.
	Tues Feb 27
	Thurs Mar 1			Local invariant features: description and matching
	Tues Mar 6		Sec 2.1.1, 2.1.2, 6.1.1, 6.1.4	Alignment
	Tues Mar 20		Sec 3.6.1	Homography and image warping	, Mar 19 Vision job talk, Tues 11 am in GDC auditorium, Shuran Song, Princeton, Seeing the Unseen: Data-Driven 3D Scene Understanding for Robot Vision
	Thurs Mar 22		Sec 11.1.1, 11.2-11.5	Stereo, part 1
	Tues Mar 27			Stereo, part 2
	Thurs Mar 29		Synthesis Ch 4, 5, 6 (pdf on Canvas) Szeliski 14.3  demo by Sivic et al.,  David Lowe's SIFT and Generalized Hough approach ( )
	Tues April 3				Vision job talk: Saraubh Gupta, UC Berkeley, 11 am GDC auditorium; "Visual Perception and Navigation in 3D Scenes"
	Tues April 10
	Tues April 17				A4 due Wed. May 2. Vision job talk, , CMU: Social signal processing: A computational approach to sensing, reconstructing, and understanding social interaction.  11 am in GDC auditorium.
	Thurs April 19
	Tues April 24
	Thurs April 26
	Tues May 1		[ ]
					Final exam is Thurs May 10, 2-5 pm

Assignments:   Assignments will be given approximately every two weeks.  The programming problems will provide hands-on experience working with techniques covered in or related to the lectures.  All code and written responses must be completed individually.  Most assignments will take significant time to complete.  Please start early, and use Piazza and/or see us during office hours for help if needed.     Please follow instructions in each assignment carefully regarding what to submit and how to submit it. Extension policy: If you turn in your assignment late, expect points to be deducted. Extensions will be considered on a case-by-case basis, but in most cases they will not be granted.    The greater the advance notice of a need for an extension, the greater the likelihood of leniency.   For programming assignments, by default, 10 points (out of 100) will be deducted for lateness for each day late.  We will use the submission program timestamp to determine time of submission.  One day late = from 1 minute to 24 hours past the deadline.  Two days late = from 24 hours and 1 minute to 48 hours past the deadline.  We will not accept assignments more than 4 days late, or once solutions have been discussed in class, whichever is sooner. Exams :  There is an in-class midterm and a comprehensive final exam.  Both exams will be offered at the listed time only.  The registrar will set our final exam date, which according to the published UT academic calendar could be as late as May 15 this year .  Please account for this when making your summer plans.  Neither exam will be offered at a different time to accommodate personal travel plans, internship start dates, interviews, etc. Participation/attendance:  Regular attendance is expected.  If for whatever reason you are absent, it is your responsibility to find out what you missed that day.  Note that attendance does factor into the final grade.  ( See Section II of the UTCS Code of Conduct regarding attendance expectations.)  General responsibilities :  Beyond the above, your responsibilities in the class are: Come to lecture on time. Check the class webpage for assignment files, notes, announcements etc. Use Piazza for class-related discussion and assignment help (no spoilers, please!) Complete the readings prior to lecture.  The reading assignments listed on the schedule should be read before the associated class lecture. Please do not use a laptop, cell phone, tablet, etc. during class. Please read and follow the UTCS code of conduct.

Important Dates

Please note the following important dates and deadlines. A0 due Tues Jan 23 A1 due Fri Feb 9 A2 due Fri Mar 2 (tentative) Midterm exam Thurs Mar 8 (in class, tentative) A3 due Fri Mar 30 Tues April 3 (tentative) A4 due Tues April 17 (tentative) A5 due Tues May 1 (tentative) Last class meeting Thurs May 3 Final exam :  Thurs May 10, 2-5 pm.  The exam is given during the normal final exam period and will be offered at that time only.  See above . Assignments are due about every two weeks.  The assignment deadlines below are tentative and are provided to help your planning.  They are subject to minor shifts if the lecture plan needs to be adjusted slightly according to our pace in class.

Grading Policy

Grades will be determined as follows.  You can check your current grades online using Canvas.

Assignments (50%, equally weighted for A1-5; 1 point for A0) Midterm exam (15%) Final exam (25%) Class participation, including attendance (10%)

Academic Dishonesty Policy

You are encouraged to discuss the readings and concepts with classmates. However, all written work and code must be your own. All work ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course.  The case will also be reported to the Office of the Dean of Students , which may institute its own disciplinary measures. If in doubt, look at the departmental guidelines and/or ask.

Notice about Students with Disabilities

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 471-6529; 471-4641 TTY. If they certify your needs, I will work with you to make appropriate arrangements.

Notice about Missed Work Due to Religious Holy Days

A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.

Latex Templates for Assignment Write-ups (Optional)

You may use any tool for preparing assignment write-ups that you like, so long as it is organized and clear.  Typically we ask for a mix of descriptions/explanations as well as embedded figures composed of images and/or plots produced in Matlab. Below we provide some info about using Overleaf, a free online editor for Latex.  Overleaf provides various Latex templates and compiles your edited .tex files into a pdf automatically.  The basics:     1) go to overleaf.com     2) sign up/sign in     3) click new project on the left     4) scroll down to "Homework Assignment" and click on "more homework assignment templates"     5) choose whichever template you feel comfortable with and click "open as template"     6) start editing     7) once you are done editing, click "PDF" in the panel above. A pdf file will be generated and downloaded automatically. Here are instructions about inserting images . How to position images . Captioning, scaling, resizing .

CS4670/5670 - Computer Vision

Humans are extremely good at perceiving the world from visual input alone. This comes so easily to us that we underestimate how difficult perception it is, and how hard it is for machines, as the webcomic above illustrates. Computer vision is a subfield of AI focussed on getting machines to see as humans do, and has been around for almost half a century. This course will cover the basics of computer vision: the underlying mechanics of images, the core problems that the field focuses on, and the array of tools and techniques that have been developed. The emphasis will be on covering the fundamentals which underly both computer vision research and applications. A tentative list of topics is below: A detailed but tentative list of learning outcomes can be found below. This course is intended for undergraduate students and MEng. students. Knowledge of basic probability and linear algebra will be useful.

MWF 1:25pm - 2:15pm Ives 305

PLEASE NOW USE THE NEW WIKI AT href=http://cirl.lcsr.jhu.edu/Vision_Syllabus and

Online-Submission | Announcements | Course Information | Syllabus | Policies | Texts | Web Links | Schedule | FAQ | TA Hours & Info

Online Submission

Announcements, course information.

Computer Vision is the study of inferring properties of the world based on one or more digital images.

This course is intended for first year graduate students and advanced undergraduates. We assume students have a rudimentary understanding of linear algebra, calculus, and are able to program in some type of structured language. There will be five to six homework assignments, an exam, and a final project. Grading will be approximately 50% on the homework assignments, 25% on the exam, and 25% on the final project.

Meetings:	TBD
Professor:	Greg Hager
E-mail:	hager at cs dot jhu dot edu
Office:	CSEB 121, Homewood Campus
Office hours:	TBD
Teaching Assistant:	TBD

Grading will be approximately 50% on the homework assignments, 25% on the exam, and 25% on the final project.

Homeworks are due on by midnight on Wednesday, if submitted online and must be turned in by 5pm Wednesday afternoon, if submitted on paper. Late homework is frowned upon. 10% of the possible grade is deducted for each day late. If there is ever a situation which prohibits you from turning in your homework on time, you must alert the Office of Student Affairs because I will check with them to verify the claims.

• Do not use work from someone else.
• Give proper credit if you do use someone else's work.

Naturally, even if you give appropriate credit, you will only receive credit for your original work, so for this class you should stick with option #1. All cases of confirmed cheating/plagiarism will be reported to the Student Ethics Board.

• see links below
• Robot Vision by BKP Horn, MIT Press, 1986.
• Machine Vision, R.C. Jain, R. Kasturi and B.G. Schunck, McGraw-Hill, 1995.
• Computer vision by Dana H. Ballard, Christopher M. Brown.
• Image processing, analysis, and machine vision by Milan Sonka, Vaclav Hlavac, and Roger Boyle.
• Stereo Matching Notes 1
• Stereo matching Notes 2
• A New GPCA Algorithm for Clustering Subspaces by Fitting, Differentiating and Dividing Polynomials.
• Motion Segmentation with Missing Data using PowerFactorization and GPCA
• Notes on Projective Geometry
• Prof. Wolff's notes from previous year's courses.
• Computer Vision -- A modern approach (draft version) by  David Forsyth and Jean Ponce.
• Computer Vision  (draft version) by George Stockman and Linda Shapiro.
• Some interesting optical illusions.
• Links to a variety of other Computer Vision courses.
• The Computer Vision Homepage (links to vision sites around the country).
• CVonline (a compendium of Computer Vision Bibliographic Information).
• Matlab Summary and Tutorial
• Matlab Tutorial
• The MathWorks - MATLAB Tutorial
• Matlab Primer
• On-line Matlab Help
• Writing Fast Matlab Code (pdf)
• Code Vectorization Guide
• Matlab Programmin Style Guidelines (pdf)

-->  

Introduction	Cameras/Radiometry/Photometric Stereo	Course Syllabus, FP 1,5, 5.4, 6, TV Ch.1,2.1-2.2, SS 6
Images and color images, cameras, etc.	Matlab.	TV Ch.3,4.1-4.2, FP 4,7
Filtering	Edge operators	TV Ch.4.3,5.1-5.2,5.5 FP 8
Edges	Edges	TV Ch.2, FP 8 FP 15.1, 15.5.2
Grouping	Grouping/Segmentation	TV Ch.6, FP 2,3	-->
Segmentation	Geometry		-->    -->
	Calibration	TV Ch.7 (not 7.3.7,7.4.3), FP 11	-->
Stereo	Stereo	TV 8-8.5.1, FP 10.1.3, 12.3, 12.4
Stereo/Motion	Motion
Motion		TV 10
Object Recognition	Object Recognition	TV A.8, FP 21.4, 22.1-3, 18	-->
Object Recognition	Object Recognition		-->
	Face Finding

Some of the matlab diaries are concatenated to the previous week's diary

Department of Computer Science The Johns Hopkins University New Engineering Building Room 224 3400 North Charles Street Baltimore, MD 21218 (410) 516-8775 (phone) (410) 516-6134 (fax) All comments may be sent via email. [email protected]

You can download the assignment here You can preview the assignment here

If you need an introduction to Python or numpy, you can check out this tutorial .

This assignment is due on Friday, February 26, 11:59:59pm .

CS231n: Deep Learning for Computer Vision

Stanford - spring 2024, assignments.

There will be three assignments which will improve both your theoretical understanding and your practical skills. All assignments will contain programming parts and written questions. For practical reasons, in office hours, TAs have been asked to not look at students’ code.

• Assignment 1 (10%): Image Classification, kNN, SVM, Softmax, Fully-Connected Neural Network
• Assignment 2 (20%): Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets, Network Visualization
• Assignment 3 (15%): Image Captioning with Vanilla RNNs, LSTMs, Transformers, Generative Adversarial Networks

All assignments are due at 11:59 PM Pacific Time. All deadlines will be posted on Ed and on the Schedule page.

Assignments are submitted via Gradescope . You will be automatically added to the course on Gradescope before the start of the quarter. If that is not the case, please email us to sort it out. If you need to sign up for a Gradescope account, please use your @stanford.edu email address. Further instructions are given in each assignment handout. Do not email us your assignments.

For submission instructions, follow the steps listed on the appropriate assignment handout.

Late Policy

See the late policy on the home page .

Collaboration Policy

Study groups are allowed and students may discuss in groups. However, we expect students to understand and complete their own assignments. Each student must write down the solutions independently (without referring to written notes from the joint session) and hand in one assignment per student. If you worked in a group, please put the names of your study group at the top of your assignment. When in doubt about collaboration details, please ask us on Ed .

Honor Code : There are a number of solutions to assignments from past offerings of CS231n that have been posted online. We are aware of this, and expect that all work submitted by students will be their own. Like all other classes at Stanford, we take the student Honor Code very seriously.

CSE455: Computer Vision

Catalog Description: Introduction to image analysis and interpreting the 3D world from image data. Topics may include segmentation, motion estimation, image mosaics, 3D-shape reconstruction, object recognition, and image retrieval. Prerequisite: CSE 333; CSE 332.

• Spring, 2024 (Krishna)
• Winter, 2024 (Shapiro)
• Spring, 2023 (Redmon)
• Winter, 2023 (Redmon)
• Spring, 2022 (Redmon)
• Winter, 2022 (Redmon)
• Spring, 2021 (Redmon)
• Winter, 2021 (Redmon)
• Spring, 2020 (Shapiro)
• Autumn, 2019 (Redmon)
• Autumn, 2018 (Martin)
• Spring, 2018 (Farhadi, Redmon)
• Winter, 2017 (Shapiro)
• Winter, 2016 (Shapiro)
• Autumn, 2014 (Shapiro)
• Autumn, 2013 (Farhadi)
• Autumn, 2012 (Seitz)
• Winter, 2012 (Seitz)
• Autumn, 2010 (Shapiro)
• Winter, 2010 (Kemelmacher, Simon)
• Winter, 2009 (Rao)
• Winter, 2008 (Seitz)
• Winter, 2007 (Shapiro)
• Winter, 2006 (Seitz)
• Winter, 2005 (Shapiro, Simon)
• Winter, 2004 (Seitz)
• Winter, 2003 (Seitz)

University of Washington - Paul G. Allen School of Computer Science & Engineering, Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX

UW Privacy Policy and UW Site Use Agreement

• Course Materials

Assignments Homework 1 Homework 2 Homework 3 Homework 4 Homework 5

Coding A majority of homework assignments will require you to write code using Python. We will use Google Colab , a free Jupyter notebook environment that runs entirely in the cloud — you just need a Google account to use Colab. Colab also comes with a free GPU, which will be useful when we are doing deep learning operations later in the course. If you are unfamiliar with Jupyter notebooks, please check out Course Materials .

Late Policy Assignments are expected to be submitted on the due date. Each student gets a total of 3 late days. All 3 days can be used towards 1 assignment, or 1 day late for 3 assignments, or other combinations. Late submissions beyond that will be penalized as below:

• One day late will be penalized 25% of the credit.
• Two Days late will be penalized 50%.
• Submissions more than 2 days late will not be considered for credit.

I will be ruthless in enforcing this policy. There will be no exceptions.

Collaboration I encourage collaboration both inside and outside class. You may talk to other students for general ideas and concepts, but you should answer questions independently and submit your own work. You will be asked to write names of any collaborators on your homework assignments.

Plagiarism Plagiarism of any form will not be tolerated. You are expected to credit all sources explicitly. If you have any doubts regarding what is and is not plagiarism, talk to me.

Comments are closed.

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EECS 442: Computer Vision (Winter 2024)

• Instructor : Jeong Joon Park
• Alex Janosi
• Anurekha Ravikumar
• Farzad Siraj
• Jinfan Zhou
• Shrikant Arvavasu
• Yuhang Ning (dlning)
• Lecture : Monday/Wednesday 10:30 AM - 12:00 Noon, STAMPS
• Monday 12:30-1:30PM, 2166 DOW - Alex
• Monday 3:30-4:30PM, 1005 DOW - Anurekha
• Wendesday 3:30-4:30PM, 3150 DOW - Farzad
• Wednesday 4:30-5:30PM, 3150 DOW - Yuhang Zoom Link
• Wednesday 5:30-6:30PM, 107 GFL - Jinfan
• Thursday 3:30-4:30PM, 3150 DOW - Shrikant
• Piazza Signup
• Lecture Recordings (It could take a couple of days to be processed and uploaded)
• Course Calendar
• Virtual Office Hours
• Course email: [email protected]
• Note on Waitlist: We DO NOT reorder waitlist. Please talk to the undergraduate advising office regarding your course enrollment.
• Homework 1: Numbers and Images
• Homework 2: Convolution and Feature Detection
• Homework 3: Fitting Models and Image Warping
• Homework 4: Machine Learning
• Homework 5: Generative Models
• Homework 6: 3D Vision

Tentative Schedule, details are subject to change. Refer to Textbooks for textbook acronyms in readings.

Date	Topic	Material
Wednesday Jan 10	Overview, Logistics, Pinhole Model, Homogeneous Coordinates	Reading: S2.1, H&Z 2, 6
Monday Jan 15	Martin Luther King Day
Wednesday Jan 17	Intrinsics & Extrinsic Matrices, Lenses	Reading: S2.1, H&Z 2, 6
Monday Jan 22	Floating point numbers, Linear Algebra, Calculus	Reading: Kolter
Wednesday Jan 24	Human Vision, Color Vision, Reflection
Monday Jan 29	Linear Filters, Blurring, Separable Filters, Gradients	Reading: S2.2, S2.3
Wednesday Jan 31	Edge Detection, Gaussian Derivatives, Harris Corners
Monday Feb 5	Scale-Space, Laplacian Blob Detection, SIFT
Wednesday Feb 7	Linear Regression, Total Least Squares, RANSAC, Hough Transform	Reading: S2.1, S6
Monday Feb 12	Affine and Perspective Transforms, Fitting Transformations	Reading: S2.1, S6
Wednesday Feb 14	Supervised Learning, Linear Regression, Regularization	Reading: ESL 3.1, 3.2(skim)
Monday Feb 19	SGD, SGD+Momentum
Wednesday Feb 21	Backpropagation, Fully Connected Neural Networks
Monday Feb 26
Wednesday Feb 28
Monday Mar 4	Convolution, Pooling
Wednesday Mar 6	CNN Architectures, Training Methods & Techniques
Monday Mar 11	Semantic/Instance Segmentation
Wednesday Mar 13
Monday Mar 18	Generative models, GANs, Self-supervised learning
Wednesday Mar 20	Score-based Models, Diffusion Models
Monday Mar 25
Wednesday Mar 27	Intro to 3D, Camera Calibration	Reading: S6.3
Monday April 1	Epipolar Geometry, The Fundamental & Essential Matrices	Reading: S11
Wednesday April 3	Two-view Stereo
Monday April 8		Reading: S7
Wednesday April 10	3D Representations, Neural 3D reconstruction
Monday April 15
Wednesday April 17		Slides
Monday April 22	Transformers, Ethics
Saturday April 29		Slides

Prerequisites

Concretely, we will assume that you are familiar with the following topics and will not review them in class:

• Programming - Algorithms and Data Structures at the level of EECS 281.
• Python - All course assignments will involve programming in Python.

It would be helpful for you to have a background in these topics. We will provide refreshers on these topics, but we will not go through a comprehensive treatment:

• Array Manipulation - Homework assignments will extensively involve manipulating multidimensional arrays with NumPy and PyTorch . Some prior exposure will be useful, but if you’ve never used them before, then the first homework assignment will help you get up to speed.
• Linear Algebra - In addition to basic matrix and vector operations, you will need to know about the cross product, eigenvectors, and singular value decomposition.
• Calculus - You should be comfortable with the chain rule, and taking partial derivatives of vector-valued functions.

Much of computer vision is applying linear algebra to real-world data. If you are unfamiliar with linear algebra or calculus, past experience suggests that you are likely to struggle with the course. If you are rusty, we will provide math refreshers on the necessary topics, however, they are not meant as a first introduction.

There is no required textbook. Particularly thorny homeworks will often come with lecture notes to help. The following optional books may be useful, and we will provide suggested reading from these books to accompany some lectures:

• Computer Vision: Algorithms and Applications by Richard Szeliski: Available for free online here . (S)
• Computer vision: A Modern Approach (Second Edition), by David Forsyth and Jean Ponce.
• Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Available for free online here . (ESL)
• Multiple View Geometry in Computer Vision (Second Edition), by Richard Hartley and Andrew Zisserman. Available for free online through the UM Library (login required) . (H&Z)
• Linear Algebra review and reference, by Zico Kolter. Available for free online here . (Kotler)

Your grade will be based on:

• Homework (60%) : There will be six homeworks over the course of the semester. Each is worth 10%.
• Midterm (20%) : There will be a midterm in-class.
• Final Project (20%) : There will be a final project, in which you work in groups of 3-4 students to produce a substantial course project over the second half of the semester. This will consist of a proposal (worth 2%), and final report and video (worth 18%).

Project Guidelines

See here for details.

Contact Hours

• Lectures : There are two sections. The lectures will be recorded and available on zoom. In person lecture attendance is optional.
• Discussions : There are six discussion sections. You are free to attend whichever you would like.
• Office Hours : Office hours are your time to ask questions one-on-one with course staff and get clarification on concepts from the course. We encourage you to go to GSI office hours for implementation questions about the homework and faculty office hours for conceptual questions.
• Piazza : The primary way to communicate with the course staff is through Piazza. The link is on canvas. We will use Piazza to make announcements about the course, such as homework releases or course project details. If you have questions about course concepts, need help with the homework, or have questions about course logistics, please post on Piazza instead of emailing course staff directly. Since Piazza is a shared discussion forum, asking and answering questions there is encouraged. On the other hand, please do not post homework solutions on Piazza . If you have questions about a particular piece of code, please make a private post.
• Email : If you need to discuss a sensitive matter that you would prefer not to be shared with the entire course staff, then please email the instructor or your section’s GSI/IA directly.

Course Policies

Formatting and submission.

Submissions that do not follow these rules (and any additional ones specified in the homeworks) will get a 0.

• No handwriting - LaTeX is not required, but encouraged. Just put some effort into generating a readable PDF.
• Mark answers on Gradescope - With a few hundred students, graders will not have time to search for answers.

Collaboration and External Sources

• Automated plagiarism detection : The vast majority of students are honorable. To ensure that honorable behavior is the incentivized behavior, we will run MOSS on the submitted homework.
• Collaboration with students : You should never know the specific implementation details of anyone else’s homework or see their code. Working in teams and giving general advice about outputs or strategies (e.g., ‘‘if the image is really dark when you merge them together, you probably have screwed up the image mask with the number of images’’) is great. However, pair-programming or sitting next to someone else and debugging their code is not allowed.
• Consulting outside material : You can and should turn to other documentation (suggested textbooks, other professors’ lecture notes or slides, documentation from libraries). You may not read a set of code (pseudocode is fine). If you come across code in your search, close the window and don’t worry about it.
• Things you should never worry about : Reading the documentation for publicly available libraries; clarifying ambiguities and mistakes in assignments, slides, handouts, textbooks, or documentation; discussion the general material; helping with things like cryptic numpy errors that are not related to class but part of the cost of doing business with a library; discussing the assignments to better understand what’s expected and general solution strategies; discussing the starter code; discussing general strategies for writing and debugging code.
• Generative AI : Tools like ChatGPT are strongly discouraged. We know we can’t stop you, however, using them will lead to you getting very little hands-on coding ability from this course and you will struggle on the midterm. The libraries used in this course are industry-standard and it is very helpful to be comfortable with them.

Late Submissions

Our policy is quite generous. Exceptions will be made in only truly exceptional circumstances by the professor.

• Late Days - 6 total late days across all homeworks. These will be applied automatically, no need to contact us. Homeworks are due by 11:59:59 on the due date. Thus, the late day would start at 12:00:00.
• Penalty - If you have 0 late days available, any subsequent late submissions will receive a 10% max score reduction per day. For example, if you submit 3 days late, you can receive at most 70% credit.
• Late Deadline - Late submissions will be accepted until a week after the deadline.
• Project - No late submissions. Late days and penalties will not be applied. This will be due as late as we can take them while still delivering grades on time.
• Method - Please submit regrade requests through Gradescope.
• Deadline - Submit regrade requests within 1 week of grades being released.
• Minor Regrades - Regrade requests that concern minor judgement calls that change the grade by <= 1 point for a problem or by <= 3 points for the whole homework will not be considered. If you believe this may affect your grade at the end of the semester, contact the professor.

Staff & Office Hours

Lecture	Date	Topic	Materials	Assignments
Mon, Aug. 30	Introduction
Lec. 2	Wed, Sep. 1	Linear filtering
Sec. 1		Linear algebra
	Mon, Sep. 6	No class - Labor Day
Lec. 3	Wed, Sep. 8	More filtering	"
Sec. 2		Fourier Transform
Lec. 4	Mon, Sep. 13	Frequency
Lec. 5	Wed, Sep. 15	Image pyramids		ps1 due
Sec. 3		Pyramids and frequency
Lec. 6	Mon, Sep. 20	Machine learning
Lec. 7	Wed, Sep. 22	Linear classifiers		ps2 due
Sec. 4		Machine learning tutorial
Lec. 8	Mon, Sep. 27	Neural networks
Lec. 9	Wed, Sep. 29	Optimization		ps3 due
Sec. 5		Backpropagation tutorial
Lec. 10	Mon, Oct. 4	Convolutional networks
Lec. 11	Wed, Oct. 6	Scene understanding		ps4 due
Sec. 6		PyTorch tutorial
Lec. 12	Mon, Oct. 11	Object detection
Lec. 13	Wed, Oct. 13	Image synthesis		ps5 due
Sec. 7		Office hours + GANs
	Mon, Oct. 18	No class - Fall Break
Lec. 14	Wed, Oct. 20	Temporal models
Sec. 8		Project office hours
Lec. 15	Mon, Oct. 25	Representation learning
Lec. 16	Wed, Oct. 27	Language		ps6 due
Sec. 9		Object detection + project office hours
Lec. 17	Mon, Nov. 1	Sound and touch
Lec. 18	Wed, Nov. 3	Motion Guest lecturer: Shu Kong
Sec. 10		Project office hours
Lec. 19	Mon, Nov. 8	Image formation
Lec. 20	Wed, Nov. 10	Multi-view geometry		ps7 due
Sec. 11		Representation learning + Project office hours
	Mon, Nov. 15	No class
Lec. 21	Wed, Nov. 17	Fitting geometric models		ps8 due
Sec. 11		Project office hours
Lec. 22	Mon, Nov. 22	Structure from motion
	Wed, Nov. 24	No class - Thanksgiving
		Project office hours
Lec. 23	Mon, Nov. 29	Light, shading, and color
Lec. 24	Wed, Dec. 1	Embodied vision		ps9 due
Sec. 12		Project office hours
Lec. 25	Mon, Dec. 6	Recent network architectures
Lec. 26	Wed, Dec. 8	Bias and disinformation		ps10 due

EECS 3312 -->

Day	Time	Name	Location
Monday	11:00am - 12:00pm	Xin Hu
	4:30pm - 5:00pm	Andrew Owens	EECS 3312
Tuesday	9:00am - 10:00am	Xin Hu
	3:00pm - 4:00pm	Tony Pan	EECS 3312
Wednesday	11:00am - 12:00pm	Nikhil Devraj	EECS 3312
	10:00am - 11:00am	Tony Pan	Zoom
Thursday	11:30am - 12:30pm	Nikhil Devraj
Friday	4:00pm - 5:00pm	Andrew Owens

Course information

Time	Place
Thu 4:00-5:00pm	1005 DOW
Fri 10:30-11:30am	1200 EECS
Fri 12:30-1:30pm	G906 COOL +

• Szeliski. Computer Vision: Algorithms and Applications, 2nd edition draft (available for free online)
• Goodfellow, Bengio, Courville. Deep Learning . (available for free online)
• Forsyth and Ponce. Computer Vision: A Modern Approach .
• Hartley and Zisserman. Multiple View Geometry in Computer Vision .

Homework	70%
Final project	30%

A+	Curved
A	92%
A-	90%
B+	88%
B	82%
B-	80%
C+	78%
C	72%
C-	70%

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Realistic mesh-based avatars. ECCV 2022

SamsungLabs/rome

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Realistic one-shot mesh-based avatars.

Paper | Project Page

This repository contains official inference code for ROME.

This code helps you to create personal avatar from just a single image. The resulted meshes can be animated and rendered with photorealistic quality.

Important disclaimer

To render ROME avatar with pretrained weights you need download FLAME model and DECA weights. DECA reconstructs a 3D head model with detailed facial geometry from a single input image for FLAME template. Also, it can be replaced by another parametric model.

Getting started

Initialise submodules and download DECA & MODNet weights

Install requirements and download ROME model: gDrive , y-disk .

Put model into data folder.

To verify the code with images run:

For linear basis download ROME model: gDrive (or camera model for voxceleb gDrive ), yDrive

This code and model are available for scientific research purposes as defined in the LICENSE file. By downloading and using the project you agree to the terms in the LICENSE and DECA LICENSE. Please note that the restriction on distributing this code for non-scientific purposes is limited.

This work is based on the great project DECA . Also we acknowledge additional projects that were essential and speed up the developement.

• DECA for FLAME regressor and useful functions
• Pytorch3D for differentiable rendering,
• face-alignment for keypoints
• VGGFace2-pytorch for identity loss
• MODNet , FaceParsing , Graphonomy for fast segmentations
• H3DNet for evaluation geometry

If you found this code helpful, please consider citing:

Contributors 2

• Python 96.4%
• Jupyter Notebook 3.1%

CS 232 Computer Organization Notes & Homework

The lecture notes and homework assignments are posted here.

• Course Overview
• Overview of Computer Organization

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• CD & DVD Copying

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Come in and see our ASUS Home and Business Computers and Acer Laptops.  Acer is the world's second largest and fastest growing computer company.  Quality speaks for itself.  You will love their line of light weight laptops with exceptionally long battery life and powerful, built-in wireless.

Being an Internet Provider we are heavily into Networking.  Come in and see what has to be the area's most extensive inventory of Networking switches, routers, wireless routers, commercial radios, fiber optic equipment and our huge cable and accessory selection.

Everything for the Do-it-Yourselfer

You can build your own computer just from the parts on our shelves! We have a large variety of parts from Motherboards to Processors to Memory to Video to Drives. And don't miss out on our attractive computer cases. With 25 years of computer building experience, everything from home computers to large Internet servers, we are the area's most trusted source of quality computer parts.

Lowest Prices in Town for Hundreds of Items

Our last price spot survey , in 2014, checked our prices both locally and on-line for three Moscow big-box stores.  We have an especially good inventory of cables and connectors, so we surveyed every tenth item in our on-line inventory's cable list.  We first checked on-line prices at Staples, Office Depot and Walmart.  For the items which were listed in their online inventory, we then physically checked each Moscow store.  If they had something similar in the store, say a cable that was a different color or slightly different in length, we accepted that item.

Results:   As had been true in earlier, less formal surveys, we have a very much larger stock of cables, and with a few exceptions, lower local prices than any of the big box stores surveyed.  We reserve the right, of course, to adjust our prices at any time, but this survey was accurate to the best of our knowledge as of 3/18/14.

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