527 Resources

Online References on Computer Vision

The Wikipedia entry on computer vision. The Wikipedia has specific entries for most CV topics as well.
The CVonline compendium of computer vision
A list of tutorials on 2D image analysis and computer vision topics maintained by the International Association for Pattern Recognition
Duke University Libraries. Click on E-Journals to find computer vision journals
The CMU Computer Vision web page is obsolete, but still has some useful data and information

Books on Computer Vision

D. A. Forsyth and J. Ponce, Computer Vision — A Modern Approach, second edition, Pearson 2012
R. Szeliski, Computer Vision — Algorithms and Applications, Springer 2011
R. Klette, Concise Computer Vision — An Introduction into Theory and Algorithms, Springer 2014
S. J. D. Prince, Computer Vision — Models, Learning, and Inference, Cambridge 2012
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge 2000
Y. Ma, S. Soatto, J. Košecká, S. S. Sastry, An Invitation to 3-D Vision — From Images to Geometric Models, Springer 2004

Programming

Anaconda is a stable and coherent distribution of Python for data science. It is strongly recommended that you use the latest version of Anaconda, rather than whatever you may have already on your computer. This distribution includes Python, several basic libraries for data science (numpy, scipy, and more), visualization libraries (including matplotlib), machine learning libraries, including scikit-learn. This is essentially all you need for this course. This distribution places all relevant files in the appropriate places, and you won't have to struggle with linking libraries, etc.
Any program that is longer than a few lines of code requires debugging, and debugging is a nightmare in a Python notebook. You are urged to download the (free) PyCharm Integrated Development Environment (IDE). If you do a lot of programming outside this course, you may want to download the professional version, which is available for free here if you access that page from a Duke computer. The professional version has tools that are very useful for professional development but you won't need in this course.
Google's Python class is a leisurely but clear Python tutorial.
The official Python 3 Documentation also includes a tutorial. Use the library reference and the language reference as your official sources of information about Python 3. You can also find information by googling, but make sure you refer to version 3 of Python if you do so.
Several tutorials on Jupyter notebooks can be found online. Here is one from Dataquest.

Software for Computer Vision

Scikit-image, a Python module for image processing.
The Open Source Computer Vision Library web page
The Insight Toolkit (ITK), an open-source, cross-platform image processing library developed by the Insight Software Consortium with many participants
ImageJ, an image processing library in Java developed by Wayne Rasband
Irfanview, a free and powerful image viewer for Windows. Use xv on Unix and ToyViewer on OS X
Andrea Vedaldi's (Oxford) VLFeat toolbox, including an implementation of the SIFT operator and much more

Online References on Machine Learning and Recognition

The Wikipedia entry on machine learning. The Wikipedia has specific entries for most ML topics as well.
A list of tutorials on machine learning topics maintained by the International Association for Pattern Recognition
Deep Learning: Methods and Applications, a monograph by Li Deng and Dong Yu
A deep learning tutorial by Stanford's Deep Learning Group
The VGG convolutional neural networks practical by Andrea Vedaldi and Andrew Zisserman (includes Matlab code)
A slide tutorial on deep belief nets by Geoffrey Hinton
A practical guide to training restricted Boltzmann machines by Geoffrey Hinton

Books on Machine Learning and Recognition

C. C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, 2018.
S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning, Cambridge University Press, 2014.
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press 2016
C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning — Data Mining, Inference, and Prediction, Springer 2001
K. P. Murphy, Machine Learning — A Probabilistic Perspective, MIT Press 2012
S. J. Dickinson, A. Leonardis, B. Schiele, and M. J. Tarr, editors, Object Categorization — Computer and Human Vision Perspectives, Cambridge 2009

Software for Machine Learning and Recognition

Scikit-learn, a Python module for machine learning
Documentation for the TreeBagger class that implements random forests in the Matlab Statistics and Machine Learning Toolbox
Pytorch, a machine learning framework developed by Facebook's AI Research Lab
TensorFlow, a machine learning framework developed by the Google Brain research team
The Caffe deep learning C++/CUDA framework (includes command line, Python, and Matlab interfaces) developed by the Berkeley Vision and Learning Center
MLPACK, a scalable C++ machine learning library developed at GeorgiaTech's fastlab
LIBSVM, a C++ and Java software library for support vector machines developed by Chih-Chung Chang and Chih-Jen Lin
SVM light, a C software library for support vector machines developed by Thorsten Joachims

Data

The ImageNet image download page (requires signup) made available by Stanford's ImageNet team/li>
PASCAL 3D+, a data set for 3D object detection in the wild developed by Yu Xiang, Roozbeh Mottaghi and Silvio Savarese
The MNIST database of handwritten digits developed by Yann LeCun, Corinna Cortes, and Christopher J. C. Burges
NIST Special Database 19 of handprinted forms and characters (for purchase)
The CIFAR-10 dataset of 60,000 images in ten classes developed by Alex Krizhevsky
The Caltech 101 collections of images in 101 categories developed by Fei-Fei Li, Rob Fergus, and Pietro Perona
The Caltech 256 collections of images in 256 categories developed by Greg Griffin, Alex Holub, and Pietro Perona
The COIL-20 database and COIL-100 database of 20 and 100 objects respectively, imaged from 72 viewing directions, developed by Sameer Nene, Shree Nayar, and Hiroshi Murase
The SpaceNet datasets of satellite imagery co-sponsored by IARPA, CosmiQ Works, and Maxar (formerly DigitalGlobe and Radiant Solutions)
The MPI Sintel Flow Dataset of synthetic video sequences annotated with true optical flow. The dataset was made by members of the Max Planck Institute for Intelligent Systems based on the animated short movie Sintel produced by the Blender Foundation
Several video datasets created by members of the Pattern Recognition and Image Processing Group at the University of Freiburg