xeransis / cvresources

A collection of my materials into Computer Vision

cvresources

A collection of my materials into Computer Vision. Computer Vision here means hardware (camera, lens, lighting, computing technologies) and software (image processing)

Limitation:

I list only the materials I have used so far. There's a lot more useful stuff out there. Please suggest your favorite resources!

Table of Contents

1. [Books]
2. [Courses]
3. [Software & Libraries]
4. [Hardware]
5. [Public datasets]
6. [Tutorials & Useful codes]
7. [Topics in Computer Vision]

1. Books

• Handbook of Machine Vision - A. Hornberg (http://as.wiley.com/WileyCDA/WileyTitle/productCd-3527405844.html)

  This is the first book I read about machine vision. It covers almost everything from Machine vision project analysis, Lens & Camera selection guide, Optics formulars, Illumination, fundamental Image processing algorithms to modern industrial applications. The book cost some money but you can file a pdf version somewhere.

• The Imaging & Vision Handbook by Stemmer Imaging (https://www.stemmer-imaging.co.uk/en/the-imaging-vision-handbook/)

  Similar to Handbook of Machine Vision but less theoretic stuff and more updated with recent applications. Get it free at Stemmer's website.

• Algorithms for Image Processing and Computer Vision (2nd Ed.) - J.R. Parker 2011 (http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470643854.html)

  This is the most useful collection of hand-on tutorials about fundamental CV algorithms. OpenCV and C are deployed for examples.

• Deep learning - Ian Goodfellow and Yoshua Bengio and Aaron Courville - 2016. (http://www.deeplearningbook.org/)

  I had a project dealing with Deep Convolutional Network for object classification so I read some chapters of this book for an understanding. Recommend these chapters: Part I(1,2,3,4,5); Part II (Chapter 6,7,8,9); Part III (14). The book is free!

• Pattern Classification - Duda & Hart * Stork (http://as.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html) I finished only the first chapter. It's painful to understand T.T

2.Courses

• Undergraduate level: Linear Algebra, Programming (C/C++/C#), Computer Architecture, Algorithms & Data Structure, Probability & Statistics, Physics.

• Others:

  • Numerical Methods
  • Intro to Digital Image Processing (ECSE-4540) Lectures, Spring 2015 (https://youtu.be/UhDlL-tLT2U?list=PLuh62Q4Sv7BUf60vkjePfcOQc8sHxmnDX)
  • CS231n: Convolutional Neural Networks for Visual Recognition (http://cs231n.stanford.edu/)

3.Software and libraries

• CVIPTools: Quick tool to test image processing algorithms. It's free to use without source code. Download here: http://cviptools.ece.siue.edu/
• C++ Bitmap Library: http://www.partow.net/programming/bitmap/index.html
• ImageJ
• Helicon (Focus stacking)
• Matlab
• Sapera Essential
• Halcon
• Tesseract: https://github.com/tesseract-ocr/tesseract
• OpenCV/EmguCV
• Caffe/Theano/Tensorflow
• Intel IPP
• Cuda Toolbox

7. Topics in Computer Vision

• Shape from focus
  • Shape from focus system. Nayar 1992. http://graphics.stanford.edu/courses/cs348b-06/homework3/Nayar_CVPR92.pdf
  • Analysis of focus measure operators for shape-from-focus. Pertuz 2013. https://goo.gl/dUwyeH
  • Software: Helicon
  • Demo code: https://www.mathworks.com/matlabcentral/fileexchange/55103-shape-from-focus