Wiki: Research Resources

Image Processing

• Michael Elad, ICIP 2017 Plenary: Sparse Modeling in Image Processing and Deep Learning (video)
• William Freeman, ICIP 2014 Plenary: Revealing What’s Hidden in Videos(related video)
• Computational Imaging Workshop 2019 (videos)
• John Loomis: Computer Vision, Courses
• Ashok Veeraraghavan (website)

Computer Vision

• Classes
  • Kristen Grauman (U Texas): Computer Vision
  • Ahmed Elgammal (Rutgers) Computer Vision
  • Noah Snavely (Cornell)Physics based Methods in Vision
  • Srinivasa Narasimhan (CMU): Computer Vision
  • Zhigang Zhu (CCNY): Computer Vision
  • Derek Hoiem (U Illinois): Interactive Art & Computational Design
  • Golan Levin (CMU): Computer Vision
  • Sinan Kalkan (Middle East Tech U): Computer Vision
  • Marc Pomplun (U Mass Boston): Computer Vision II
  • Robert Collins (Penn State):
• Nathan Jacobs (U Kentucky): Computer Vision
  • Deva Ramanan (UC Irvine): Computer Vision
  • Alexandra Branzan Albu (U Victoria): Computer Vision
  • Selim Aksoy (Bilkent U): Image and Video Computing
  • Margrit Betke (Boston U): Computer Vision
  • Rob Fergus (NYU): Computer Processing of Pictorial Information
  • Yiannis Aloimonos (U Maryland): Computer Vision
  • George Bebis (U Nevada): Advanced Topic in Computer Vision
  • Ronen Basri , Yaron Caspi , Meirav Galun , Michal Irani, Anat Levin and Shimon Ullman (Weizmann Institute of Science): Courses
• Lectures by famous people
  • Marc Pollefeys (U North Carolaina): http://www.cs.unc.edu/~marc/teaching.html
  • Steve Seitz (U Washington): http://www.cs.washington.edu/homes/seitz/
  • Richard Szeliski (Microsoft Research): http://research.microsoft.com/en-us/um/people/szeliski/
  • David Forsyth (U Illinois): http://luthuli.cs.uiuc.edu/~daf/
  • Richard Hartley (Australian National University): http://users.cecs.anu.edu.au/~hartley/
  • Andrew Zisserman (Oxford U): http://www.robots.ox.ac.uk/~az/
  • Marshall Tappen (U Central Florida): http://www.cs.ucf.edu/~mtappen/cap5415/
  • Larry Davis (U Maryland): http://www.umiacs.umd.edu/~lsd/426/426.html
  • Fei-Fei Li (Stanford University): http://vision.stanford.edu/teaching/cs223b/
  • Jean Ponce (U Illinois):http://www.di.ens.fr/~ponce/
  • Qi Tian (U Texas): http://www.cs.utsa.edu/~qitian/#teaching
  • Luc Van Gool (Swiss Federal Institute of Technology Zurich): http://www.vision.ee.ethz.ch/members/get_member.cgi?id=1
  • David Lowe (U British Columbia): http://www.cs.ubc.ca/~lowe/
  • Stefan Roth (TU Darmstadt): http://www.gris.informatik.tu-darmstadt.de/~sroth/teaching.html
• Tutorials
  • CVPR2010
    • David G. Stork: Computer Vision in the Analysis of Master Drawings and Paintings
    • Francisco Escolano (U Alicante): Advanced ITinCVPR in a Nutshell
    • Michal Haindl,Ji Filip (UTIA): Bidirectional Texture Function Modeling More
    • Andrea Vedaldi, Brian Fulkerson, Gary Bradski, Vadim Pisarevsky, Radu Rusu, Hailin Jin: Open Source Vision Software, Intro and Training (Recommended!)
    • Amit Agrawal, Ashok Veeraraghavan, Srinivasa Narasimhan and Ankit Mohan: Coded Computational Imaging
    • Carlos Hernández, George Vogiatzis, Yasutaka Furukawa: 3d Shape Reconstruction from Photographs: a Multi-View Stereo Approach
    • Till Quack, Jan Erik Solem: Learnings from Founding a Computer Vision Startup
    • Juyang Weng: General-Purpose Vision Architecture, Invariance, Attention and Reasoning (Slides)
    • Amir Saffari, Christian Leistner, Horst Bischof: Semi-supervised Learning in Vision
    • Michael Goesele, Hendrik P.A. Lensch, Noah Snavely: Scene Reconstruction from Community Photo Collections
    • Carsten Rother, Sebastian Nowozin: Tractable Higher Order Models in Computer Vision
    • Alexander Sorokin Fei-Fei Li: Mechanical Turk for Computer Vision
  • CVPR2009
    • Christoph H. Lampert, Matthew B. Blaschko: Kernel methods in Computer Vision
    • Noah Snavely and James Hays (Cornell):Photo Tourism and im2gps: 3D Reconstruction and Geolocation of Internet Photo Collections
    • Ramesh Raskar, Se Baek Oh, Anthony Accardi(MIT) and Zhengyun Zhang(Stanford): Light Fields: Present and Future (Computational Photography)
    • Jan-Michael Frahm(University of North Carolina), P. J. Narayanan(IIIT), Joe Stam(Nvidia Corporation): Computer Vision on GPUs
• CVPR2008
  • Amit Agrawal(Mitsubishi Electric Research Labs ) Ramesh Raskar (MIT Media Lab): Differential Techniques for Analysis and Synthesis in Vision and Graphics
  • Ardy Goshtasby (Wright University): Survey and Recent Advances in Image Registration and Fusion
  • Todor Georgiev (Adobe Systems): Theory and Methods of Light-Field Photography
• ICCV2009
  • Pushmeet Kohli, M Pawan Kumar, Carsten Rother: MAP Inference in Discrete Models (Recommended!)
    • Thomas Brox, Andrés Bruhn: Variational Optical Flow Estimation
    • Hamid Aghajan, Nicu Sebe: Human-centered Vision Systems
    • Siwei Lyu, Stefan Roth: Modeling Natural Image Statistics for Computer Vision
    • Cees G. M. Snoek, Theo Gevers, Arnold W. M. Smeulders: Coloring Visual Search
    • Francis Bach, Julien Mairal, Jean Ponce, Guillermo Sapiro: Sparse Coding and Dictionary Learning for Image Analysis
    • Marcus A. Brubaker, Leonid Sigal, David J. Fleet: Physics-Based Human Motion Modelling for People Tracking
    • Tibério Caetano, Richard Hartley: Structured Prediction in Computer Vision
    • Tae-Kyun Kim, Jamie Shotton, Björn Stenger: Boosting and Random Forest for Visual Recognition
    • Michael Bronstein, Alexander Bronstein: Numerical Geometry of Non-Rigid Objects
    • Li Fei-Fei, Rob Fergus, Antonio Torralba: Recognizing and Learning Object Categories
• ICCV2007
  • Tim Weyrich, Jason Lawrence, Hendrik P.A. Lensch, Szymon Rusinkiewicz, Todd Zickler: Principles of Appearance Acquisition and Representation
    • Jiri Matas, Krystian Mikolajczyk: Visual Recognition
    • Nikos Komodakis, Philip Torr, Vladimir Kolmogorov, Yuri Boykov: Discrete Optimization in Computer Vision
• ECCV2010
  • Daniel Cremers (Technische Universität München), Thomas Pock (Graz University of Technology) and Bastian Goldücke (Technische Universität München): Variational Methods for Computer Vision
  • Radu Bogdan Rusu, Gary Bradski, Caroline Pantofaru (Willow Garage), Stefan Hinterstoisser, Stefan Holzer (Technische Universität München), Kurt Konolige (Willow Garage) and Andrea Vedaldi (Oxford University): Computer Vision and 3D Perception for Robotics
  • Yanxi Liu (Pennsylvania State University): Computational Symmetry: Past, Current, Future
  • Michael Bronstein (Technion) and Radu Horaud (INRIA Rhône-Alpes): Diffusion Geometry in Shape Analysis
  • Kai Yu (NEC Labs) and Andrew Ng (Stanford University): Feature Learning for Image Classification
  • Ivan Laptev (INRIA/ENS) and Greg Mori (Simon Fraser University): Statistical and Structural Recognition of Human Actions
  • Michael Werman, Ofir Pele (Hebrew University of Jerusalem), Brian Kulis (University of California at Berkeley): Distance Functions and Metric Learning
  • Yaser Sheikh (Carnegie Mellon University) and Sohaib Khan (Lahore University of Management Sciences): Nonrigid Structure from Motion
• ECCV2008
  • Bruno Levy (INRIA): Mesh processing
    • Leo Grady (Siemens Corporate Research) & Sebastien Bougleux: Combinatorial Calculus in Computer Vision: Formulating and Solving Continuous PDEs on Graphs
    • John K. Tsotsos, Neil Bruce & Albert L. Rothenstein (York University): Visual attention: biology, computational models, and applications
    • Pawan Mudigonda (University of Oxford) & Pushmeet Kohli (Microsoft Research): MAP Estimation Algorithms in Computer Vision
    • Simon Thorpe (CNRS): Biological Vision : A Source of Insights for Computer Science
• CVPR2007, ICML2008, ICCV2009
  • Li Fei-Fei (Stanford), Rob Fergus (NYU), Antonio Torralba (MIT): Recognizing and Learning Object Categories
• CVPR2008, CVPR2007
  • Rene Vidal (Johns Hopkins University Center for Imaging Science) Yi Ma (University of Illinois at Urbana-Champaign): Generalized Principal Component Analysis
• CVPR2009, CVPR2010
  • Cees G.M. Snoek and Arnold W.M. Smeulders(University of Amsterdam):Video Search Engines, More

Demosaicking (Reading List)

• Hamilton Adams gradient 1997
• Glotzbach high frequency cancellation 2001
• Gunturk, Glotzbach Survey 2005
• Gunturk POCS 2005
• Hirakawa 2005, Adaptive Homogeneity Directed Demosaicking (AHD)
• Alleysson Opponent color space
• Dubois Frequency Selection
• Hirakawa joint demosaicking and denoising
• Hirakawa wavelet
• Hirakawa CFA
• Korneliussen Hirakawa PSDD
• Kiku Residual interpolation
• Zhang Hirakawa wavelet

Denoising (Reading List)

Deblurring (Reading List)

Dynamic Vision Cameras

• Prophesee knowledge base
  • ???

Time of Flight Cameras

• Texas Instruments: document 1 document 2
• Achuta Kadambi (MIT)
• http://web.media.mit.edu/~achoo/temp/global_direct_cvpr15.pdf
• https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/chr_mpi_cvpr_15.pdf
• http://web.media.mit.edu/~achoo/iccvtoftutorial/
• http://web.media.mit.edu/~achoo/nanophotography/

Deep Learning

• Github Awesome Deep Learning Papers (collection of papers since 2012)
• MIT 6.S191 Introduction to Deep Learning
• Stanford CS231a: Computer Vision, from 3D Reconstruction to Recognition
• Stanford CS231n: Convolutional Neural Networks for Visual Recognition
• ICCV 2017 tutorial: Generative Adversarial Networks
• MIT 6.S094 Deep Learning for Self Driving Cars
• Stanford CS224n: Natural Language Processing with Deep Learning
• MIT 6.034: Artificial Intelligence

Matlab

• Matlab Deep Learning Package
• Two hour training
• GPU Coder
• Examples
• Benchmark

Android Programming

Christina, add stuff here!

Datasets

• CARLA Simulator (automotive self navigation simulator) (documentation)
• GPU benchmarks

Textbooks

• Math
  • Golub, Van Loan, Matrix Computations
  • Hoffman, Kunze, Linear Algebra
• Probability, Statistical Signal Processing
  • Stark, Woods, Probability and Random Processes For Engineers
  • Papoulis, Probability, Random Variables and Stochastic Processes
  • Poor, An Introduction to Signal Detection and Estimation
  • Duda, Hart, Stork, Pattern Classification
  • Kailath, Sayed, Hassibi, Linear Estimation
• Image Processing
  • Jain, Fundamentals of Digital Image Processing
  • Lee, Introduction to Color Imaging Science
  • Farid, Photo Forensics
• Computer Vision
  • Forsyth, Ponce, Computer Vision: A Modern Approach
  • Hartley, Zisserman, Multiple View Geometry in Computer Vision

Tutorials

Conference Tutorials