Books
Computer vision:
D. Forsyth and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2002
R. Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004
Machine learning:
K. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
C. Bishop, Pattern Recognition and Machine Learning, Springer 2007
T. Mitchell, Machine Learning, McGraw-Hill, 1997
Related Courses
Some other courses for robot vision and more!
http://nameless.cis.udel.edu/class_wiki/index.php/CISC849_S2014
https://www.eecis.udel.edu/~cer/arv/
https://www.coursera.org/learn/robotics-perception/home/welcome
https://classroom.udacity.com/courses/ud810 [computer vision]
https://classroom.udacity.com/courses/ud730 [deep learning]
https://classroom.udacity.com/courses/ud120/ [machine learning]
https://classroom.udacity.com/courses/ud675 [machine learning: supervised]
https://classroom.udacity.com/courses/ud741 [machine learning: unsupervised]
https://www.edx.org/course/robotics-vision-intelligence-machine-pennx-robo2x
http://graphics.stanford.edu/courses/cs205a-17-spring/schedule.html
http://ece631web.groups.et.byu.net/
http://people.csail.mit.edu/hchengwang/courses.html
https://moocs.qut.edu.au/learn/robotic-vision-october-2016
https://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA
http://www.cs.ucf.edu/courses/cap4453/
http://www.cs.ucf.edu/~bgong/CAP4453.html
Programming resources
MATLAB “Must-Know” Tips: http://www.mathworks.com/matlabcentral/fileexchange/5685-writing-fast-matlab-code
OpenCV: http://opencv.org/
Deep learning software links: http://deeplearning.net/software_links/
Example code for homework: diffpic.c, diffpic3.c, rawtopgm.c, pgmtoraw.c, sobel.c, marrh.c
Example input images for homework: garb34.pgm, garb34, face05.pgm, face05, pipes.pgm, pipes.raw, pic14.pgm, pic14.raw, garb34rot.pgm, garb34rot.raw, cola.ppm, colb.ppm, colc.ppm, cold.ppm, cole.ppm
Convolutional Neural Networks (CNN)
[LeNet] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, november 1998.
CNN Architecture, convolution, and pooling by Andrej Karpathy: http://cs231n.github.io/convolutional-networks/
VGG CNN practical by Andrea Vedaldi and Andrew Zisserman: http://www.robots.ox.ac.uk/~vgg/practicals/cnn/
Intro to ConvNets by Kashif Rasul: https://www.youtube.com/watch?v=W9_SNGymRwo
A lecture on CNN by Nando de Freitas: https://www.youtube.com/watch?v=bEUX_56Lojc
Top computer vision conferences
CVPR: IEEE Conference on Computer Vision and Pattern Recognition
ICCV: International Conference on Computer Vision
ECCV: European Conference on Computer Vision
NIPS: Neural Information Processing Systems
SIGGRAPH: ACM Special Interest Group on Computer Graphics