Introduction to Computer Vision
This course provides an introduction to computer vision including fundamentals, methods for application and machine learning classification.
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Reddit Posts and Comments
0 posts • 35 mentions • top 19 shown below
2 points • consciousbot
Take a look at Udacity: https://www.udacity.com/course/introduction-to-computer-vision--ud810
Their introduction to computer vision is going to cover the basics of old school cv pre deep learning era. It has a couple of quizzes and exercises that really help you learn.
I used this back in 2016 while taking a grad level cv class at U C San Diego. It really helped me learn and refresh relevant content.
2 points • wafflero
Sorry for late reply. I’m using udacity/georgia tech’s course https://www.udacity.com/course/introduction-to-computer-vision--ud810
2 points • Lord_Robot
Georgie Tech (Aaron Bobick)'s computer vision course on Udacity.
Bobick covers a lot of material in reasonable depth, and has a great sense of humour!
1 points • eracro
For the core computer vision and it’s application in machine learning ( not derp learning yet ) this course by Aaron Bobick is my favorite :
It explains everything from the very beginning and ends up givig you a complete vision of the field. The assignments are various and interresting. Furthermore you can take it for free on udacity !
After that Cs231n at Sanford is really nice for deep learning I think, you will not learn the same thing at all.
Even if Deep Learning is an amazing tool for cv, the more classic tool I learnt in Bobick class actually helped me a lot in pratical applications
3 points • Andrei7777777
Looking for a free online computer vision course
I'm looking for a computer vision course but I don't see much in terms of reviews. I found 2 courses thus far:
Has anyone here taken either one of these? If you have, would you care to comment/give a rating?
Other than that, what would you recommend as a learning tool for someone wanting to get into the field.
1 points • phileas2
Computer Vision is a very futuristic field related to Practical Deep Learning and it is quite interesting ... Can you pleeeeease upload this course or any similar/better one .. I need it badly https://www.udacity.com/course/introduction-to-computer-vision--ud810
Thank you in advance !
13 points • anon35201
Step 1: Drink enough Caffeine until you feel fireworks in your head, the 1,3,7 Trimethlyxanthine molecules will occupy your Adenosine receptors and hold open your axon hillocks, putting you in a fight or flight mode, and you'll get up to speed more quickly.
Step 2: Read and consume this Whitepaper on using Convolutional Neural Nets to do digit recognition in natural photographs: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42241.pdf If you're not a person who's used to consuming whitepapers, this should take you about 5 hours to start seeing the genius in it. Chase down every keyword you don't understand, This whitepaper is a contribution to the field, and if your jaw isn't hanging open in awe, then keep trying to understand until you get there.
Step 3: Watch Andrej Karpathy's Lecture for a visual guide on how to proceed: https://www.youtube.com/watch?v=LxfUGhug-iQ
Step 4: Download this Data set of pictures of numbers in natural photographs from: http://ufldl.stanford.edu/housenumbers Organize a handful of images of numbers in natural photographs with the ground truths (correct classifications for each image) to start off you only need a few, later you will feed in tens of thousands. This data import process will need to be automated to collect the tens of thousands of images plus ground truths that the CNN needs to train.
Step 5: Get a free Github or bitbucket repository and commit all code there. Get Python 2 and Keras installed on your rig and an editor for Python code.
Step 6: Make a hello world Python2 program that imports Keras library and follow instructions online to do a Keras hello world.
Step 7: Implement this 11 line CNN in Keras to get accustomed with the tools you will be using. http://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines
Step 8: Now you're somewhat familiar with the capabilities of Keras hello world CNN, implement a VGG16 Convolutional nerual net as defined by: https://keras.io/applications/#vgg16
Step 9: Point the CNN train algorithm at the dataset. Run it. Realize that even your beefy desktop computer doesn't have enough horsepower for even a smallish datasets to complete in under 4 hours. CNN training is an extremely heavy operation and 8 hour wait times is normal. So Amazon rush a $8500 server rig with 8 slots for 8 Titan X GPUs. Guide here: https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning This thing will make a ruckus so put it in your closet with a 10k BTU air conditioner. Use an ax to poke a hole in your wall to pipe the heat outside. Put linux on it and remote login from your laptop or desktop.
Step 10: Follow the instructions to parallelize the CNN across the 8 GPU's. Train it. Show a scatter plot plotting accuracy of classification on the vertical over CNN training epochs. You should see continual improvement as training progresses. If you see improvement, tease it to approach 100% accuracy.
Step 11: Make a Blog, or a PDF or some public exposed medium where you explain everything you did from start to finish, what you expected, and what you got. This will help your learning later because you can go back and see where you failed.
Step 12: Make a Youtube Video of the CNN training. Show correctly identified instances, show incorrectly identified instances.
If you get stuck in doing these steps, (You can't get a foot hold in it or you get impossibly mired in the weeds), then you don't have what it takes to get a foothold into this subject, it's nothing to be ashamed of, you might as well be asking how to do brain surgery. In that case you should save up 700 bones to to take a formal class in this material. The keywords you need to search for are "Computer Vision and Machine Learning. I suggest: https://www.udacity.com/course/introduction-to-computer-vision--ud810 Paying for the class is the secret sauce because then your progress is forced by deadlines and letter grades and you will irritate yourself to quickly do the textbook reading, lecture viewing, homework practice and project work to get up to speed on the mathematics and calculus 3 around optics, the application of advanced mathematics and convolutional neural networks on natural images.
Convolutional Neural Networks have and continue to prove themselves as a useful data pipeline and algorithm that is making human level tasks of classifying things in images, previously thought impossible, possible. CNN's have achieved super human levels of visual processing on natural scenes in a few narrow fields, and those narrow areas are widening every month. Hurry up and get in the game. https://youtu.be/iL_TBWHR7k0?t=14s
2 points • Ajit-M
I would recommend the Udacity and Georgia Tech's Introduction to Computer Vision Lectures by Prof Aaron Bobick. Although this is more theory focused it will help you understand basics.
Udacity's Free Course - https://www.udacity.com/course/introduction-to-computer-vision--ud810
1 points • shandytp
2 points • goktugkt
If the point is stationary and your camera is moving. You can take two images consecutively and triangulate the points to find distance.
-Find orb features in two images (opencv orb) -match points (opencv bfmatcher) -apply ransac algorithm with fundemental matrix to find inliers (scikit image) -triangulate points You may need to calibrate your camera ( opencv camera calibration)
you can use aruco markers (https://docs.opencv.org/3.1.0/d5/dae/tutorial_aruco_detection.html)
*Books and courses on topic -Multiple view geometry in computer vision (book) -https://m.youtube.com/watch?v=RDkwklFGMfo (course) -https://www.udacity.com/course/introduction-to-computer-vision--ud810
1 points • muttonyKebab
1 points • 4rch_N3m3515
What about computer vision: https://www.udacity.com/course/introduction-to-computer-vision--ud810
1 points • mgamal96
Funny, I am actually in a similar situation. Though my computer vision masters begins in September. I had also begun with the textbook approach but soon moved to this online course taught by a Georgia tech prof ( https://www.udacity.com/course/introduction-to-computer-vision--ud810).
The material covered is actually very comprehensive and I found that the "geometric computer vision" topics (i.e. camera calibration and projective transformations) was taught particularly well.
2 points • thiakx
Just start viewing the videos on udacity and familiarize yourself with openCV. You will be fine.
1 points • anon35202
If it's fun why not get in the game? Go to google scholar and start searching: See second link: https://scholar.google.com/scholar?hl=en&q=external+sensor+self+driving+car
And the second link from:
Wireless sensor networks (WSNs) offer the potential to significantly improve the efficiency of existing transportation systems. Currently, collecting traffic data for traffic planning and management is achieved mostly through wired sensors. The equipment and maintenance cost and time-consuming installations of existing sensing systems prevent large-scale deployment of real-time traffic monitoring and control. Small wireless sensors with integrated sensing, computing, and wireless communication capabilities offer tremendous advantages in low cost and easy installation.
Get in the game, take a class: https://www.udacity.com/course/introduction-to-computer-vision--ud810
1 points • elias_ronin
Computer Vision is a part of Neural Networks.
You've learned Algorithms from the ML course. If you know everything about NN AND if you know PyTorch then it's not for you. But if you don't then give it a try.
There is not much on this course about Computer Vision but you need good fundamentals in order to go for it. PyTorch is more pythonic (for me) than TensorFlow, so you'll need it.
Now I'm in the Intel Edge scholarship and it's all about Computer Vision. It's easy (let's say!) for me because I had good fundamentals.
My advice is to do this course and then take some other from the Free courses that are available on Udacity, like:
2 points • CyberByte
We have a Getting Started section on our wiki.
For machine learning, I can really recommend Andrew Ng's introduction course, and then maybe deeplearning.ai (although I haven't tried it). Also, here are a bunch of free books. I recommend Elements of Statistical Learning, but from the titles it seems a few others may be more introductory.
For things like image recognition, object detection and computer vision in general, you may want to check out /r/computervision. I haven't personally tried these, but there are also courses on Udacity and Coursera. There's also this book of which you can freely download the draft version.
1 points • yasserius
- Building from scratch helps you understand the nuts and bolts, but ultimately at your job, you will be using high level, abstract APIs like Tensorflow Object Detection API.
- Check out the object detection tutorial:https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/object_detection_tutorial.ipynbThis should give you a hands on experience on how to train a bounding box detector (the most popular application of CV)
- It will be sort of repetitive, but the jupyter notebook assignments of CS231N are excellent and goes deep into important algos.
Some other resources:
- (FREE) Udacity Computer Vision: learn some non-neural CV from the old days (what's SLAM? what's disparity? HoG??? Sift what??????)
- (Also free) Computer Vision: Algorithms and Applications, 2nd ed.: This book is really good for referencing the various CV methods, from old to new.
- If you are interested in 3D computer vision, like 3D bounding box detection, check out this Multiview Geometry Course.
1 points • enes81
I also wanna start a CV course in this time. I couldn't decide yet but here is a list of courses I found.