ECE 6123 – Image and Video Processing (Spring 2021)

Course Description: 
This course introduces fundamentals of image and video processing, including color image capture and representation; contrast enhancement; spatial domain filtering; two-dimensional (2D) Fourier transform and frequency domain interpretation of linear convolution; image sampling and resizing; multi-resolution image representation using pyramid and wavelet transforms; feature point detection and feature correspondence; geometric transformation, image registration, and image stitching; selected advanced image processing techniques (sparsity-based image recovery); video motion characterization and estimation; video stabilization and panoramic view generation; basic image compression techniques and standards (JPEG and JPEG2000 standard); video compression using adaptive spatial and temporal prediction; video coding standards (MPEGx/H26x); Stereo and multi- view image and video processing (depth from disparity, disparity estimation, video synthesis, compression). Basics of deep learning for image processing will also be introduced.  Students will learn to implement selected algorithms in Python.   Prior experience with Python and deep learning are not required. You will learn as the course progresses. A class project, preferably in teams of 2 to 3 people,  is required.

Prerequisites: 
Graduate status. ECE-GY 6113 and ECE-GY 6303 preferred but not required. Should have good background in linear algebra. Undergraduate students must have completed EE-UY 3054 Signals and systems and EE-UY 2233 Probability, and linear algebra.

Instructor: 
Professor Yao Wang, 370 Jay Street, Rm 957, (646)-997-3469, Email: yaowang at nyu.edu. Homepage 

Teaching Assistants:
Zhipeng Fan, Email: zf606 at nyu.edu

Yicheng Ma, Email: ym1956 at nyu.edu

Course Schedule: 
Thursday 2:00 PM – 4:30 PM, 2-MTC 9.011, Brooklyn. Also via zoom through NYU classes

Office Hour (via Zoom from NYU Class): 
Yao Wang: Mon 4-5 PM, Tue 8-9 AM, Wed 4-5 PM or appointment by email. Zoom link: TBA

Zhipeng Fan: Tue 4-5 PM and Fri 3-4 PM. 

Yicheng Ma: Thu 8-9 AM and Wed 8-9 AM. 

Text Book/References: 

  1. Richard Szeliski, Computer Vision: Algorithms and Applications. (Available online:”Link“) (Cover most of the material, except sparsity-based image processing and image and video coding)
  2. (Optional) Y. Wang, J. Ostermann, and Y.Q.Zhang, Video Processing and Communications. Prentice Hall, 2002. “Link” (Reference for image and video coding, motion estimation, and stereo)
  3. (Optional) R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, (3rd Edition) 2008. ISBN number 9780131687288. “Link” (Good reference for basic image processing, wavelet transforms and image coding).

Course Structure: 
The class will consist of weekly lectures, weekly written homework assignments, 6 computer assignments, a team project (2-3 people in a team) and one exam. There will be two optional tutorials outside the class time, one to introduce Python programing, another to introduce PyTorch and Google Cloud Platform.

Grading Policy: 
Exam (1 exam): 30%, Final Project: 30%, Programming assignments: 30%, Written assignments: 10%. Project grade depends on project proposal, project presentation, final report, and technical accomplishment.

Attendance Policy: 
Students are encouraged to attend the class synchronously (at the scheduled class time) either in-person, or remotely through zoom if your time zone allows synchronous attending. This will help your time management and enable you to get most out of the class.  In-person classes will be recorded for reviewing afterwards, and to facilitate the learning by students in different time zones where attending synchronously is difficult.  

Homework Policy: 
Written HW will be assigned after each lecture and due at the beginning of the following lecture time (to be uploaded through NYUclasses). Programming assignment will be due as posted and will be submitted through NYUclasses. Each assignment counts for 10 points. Late submission of written assignment and programming assignment will be accepted up to 3 days late, with 2 pt deduction for each day.  Students can work in teams, but you must submit you own solutions. Solutions to the written and computer assignments will be posted 1 week after the due date. We will aim to complete the grading of each assignment within 1 week as well.

Project Guideline: Link

Suggested Project List: Link (Updated 2/23/2021)

Sample Data: 
Sample Images
Middelbury Stereo Image Database

Links to Resources (lecture notes) in Previous Offerings: 

Other Useful Links 

Tentative Course Schedule 

  • Week 1 (1/28): Course introduction. Lecture note (Updated 1/24/2021) Part 1: Image Formation and Representation: 3D to 2D projection, photometric image formation, trichromatic color representation, video format (SD, HD, UHD, HDR). Lecture note (Updated 1/24/2021). Part 2: Contrast enhancement (concept of histogram, nonlinear mapping, histogram equalization). Lecture note (Updated 1/24/2021)
     
  • Tutorial on python (1/29, 9:30 AM-11:00 AM). Materials (Updated 1/26/2021)
     
  • Computer assignment 1 (Learning Python and histogram equalization) (Due 2/11)
     
  • Week 2 (2/4): Review of 1D Fourier transform and convolution. Concept of spatial frequency. Continuous and Discrete Space 2D Fourier transform. Lecture note: “FT.pdf” (updated 2/1/2021)  2D convolution and its interpretation in frequency domain. Implementation of 2D convolution. Separable filters. Frequency response.Lecture note: “convolution.pdf” (updated 2/1/2021) Linear filtering (2D convolution) for noise removal, image sharpening, and edge detection. Gaussian filters, DOG and LOG filters as image gradient operators. Lecture note: “filtering_edge detection.pdf” (updated 2/1/2021)
     
  • Computer assignment 2 (2D filtering) (Due 2/25)
     
  • Week 3 (2/11): Image sampling and resizing. Antialiasing and interpolation filters. Spatial and temporal resolutions  of human visual systems.   Lecture note on ImageSampling (updated 2/7/21). Reference materials (updated 2/15/19):  Selesnick_MultirateSystems, Selesnick_SamplingTheorem
     
  • 2/18 Monday schedule. No class
     
  • Week 4 (2/25):  Multi-resolution representation: Pyramid and Wavelet Transforms.     Lecture note on Wavelet (updated 2/20/2021).
     
  • Programming assignment 3 (Pyramids and wavelet transforms) (Due 3/11)
     
  • Week 5 (3/4): Overview of machine learning, neural networks, convolutional networks. Convolutional Network for classification. Training and validation. Lecture note on CNN (updated 2/28/2021)
     
  • Week 6 (3/11): Convolutional Networks for Image Processing, including segmentation, denoising, object detection. part2 (updated 3/7/2021)
     
  • Tutorial on using PyTorch and Google Cloud Platform for deep learning (3/12, 9:30AM-11:00AM) Materials (updated 3/11/2021)
     
  • Programming assignment 4 (Training a U-Net for image segmentation) (Due 4/8)
     
  • Week 7: Must have formed a project team and had ideas of what project will you do. Must schedule an individual meeting with the instructor to discuss your project ideas. 
     
  • Week 7 (3/18): Feature detection (Harris corner, scale space, SIFT), feature descriptors (SIFT). Bag of Visual Word representation for image classification.  Lecture note on Features  (updated 3/17/2021)
     
  • Week 8 (3/25): Geometric mapping (affine, homography), Feature based camera motion estimation (RANSAC). Image warping. Image registration. Panoramic view stitching.  Lecture note (updated 3/24/2021)
     
  • Week 8 (3/25): Project proposal due (You should prepare the proposal following the format described in project guideline. You should have read a couple of reference papers and a detailed milestone chart and partition of project roles among the project team members).
     
  • Programming assignment 5 (Due 4/29): Stitching a panoramic picture (Feature detection, finding global mapping, warping, combining).
     
  • Week 9 (4/1): Dense motion/displacement estimation: optical flow equation, optical flow estimation (Lucas-Kanade method, KLT tracker); block matching, multi-resolution estimation. Deformable registration (medical applications). Deep learning approach. Lecture note. (updated 3/31/2021)
     
  • Week 10 (4/8): Moving object detection (background/foreground separation). Global camera motion estimation from optical flows. Video stabilization. Video scene change detection.  Lecture note. (updated 4/7/2021)
     
  • Week 11 (4/15): Image representation using orthonormal transform and dictionary. DCT and KLT; Transform-based image coding. Lecture note on transform (updated 4/14/2021)
     
  • Week 12 (4/22): Video Coding Part 1: block-based motion-compensated prediction and interpolation, adaptive spatial prediction, block-based hybrid video coding, rate-distortion optimized mode selection, rate control, Group of pictures (GoP) structure, the tradeoff between coding efficiency, delay, and complexity. Lecture note (updated 4/19/2021) 
     
  • Week 13 (4/29): Video Coding Part 2: Overview of video coding standards (AVC/H.264, HEVC/H.265); Layered video coding: general concept and H.264/SVC. Multiview video compression. Lecture note (updated 4/29/2021)
     
  • Programming assignment 6 (Due 5/17): Video Coding
     
  • 5/2 (Sunday): Exam 9 AM-11:40 AM (including all material in Weeks 1-12)
     
  • Week 14 (5/6): Stereo and multiview video: depth from disparity, disparity estimation, view synthesis. Multiview video compression. Depth camera (Kinect). 360 video camera and view stitching.  Lecture note. (updated 5/5/2021)
     
  • Week 15 (5/13): Project Presentation.
     
  • 5/18: Project Report and all other material must be uploaded.
     

Sample Exams: 

 

Sample Images: 

Policy on Academic Integrity: 
The School of Engineering encourages academic excellence in an environment that promotes honesty, integrity, and fairness. Please see the policy on academic dishonesty: Link to NYU Tandon Policy,  Link to NYU Policy.

Inclusion Statement:

The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs, ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations, and abilities will be treated with respect.   It is my intent that all students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit.  If this standard is not being upheld, please feel free to speak with me. Please visit this link for NYU Tandon’s effort in diversity and inclusion.

Moses Center Statement of Disability:

If you are student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities (CSD) at 212-998-4980 or mosescsd@nyu.edu.  You must be registered with CSD to receive accommodations.  Information about the Moses Center can be found at www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 3rd floor.