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I think the question is a little too unspecific for there to be a good answer. The field is vast and depending on which thing in computer vision you want to tac

by rsp1984 9y ago

I think the question is a little too unspecific for there to be a good answer. The field is vast and depending on which thing in computer vision you want to tackle the best learning paths may vary greatly. Just to give a bit of an overview: Before the Deep Learning Craze started in 2011 more classical Machine Learning techniques were used in CV: Support Vector Machines, Boosting, Decision Trees, etc.. These were (and still are!) used as a high level component in areas like recognition, retrieval, segmentation, object tracking. But there's also a whole field of CV that doesn't require Machine Learning learning at all (although it can benefit from it in some cases). This is typically the area of geometrical CV, like SLAM, 3D reconstruction, Structure from Motion and (Multi-View) Stereo, anything generally where you can write a (differentiable) model of reality yourself using hand-coded formulas and heuristics and then use standard solvers to obtain the model parameters given the data. Whenever it's too hard to do that (for example trying to recognize many different things in images) you need a data-driven / machine learning approach where the computer comes up with the model itself after seeing lots of training examples. As for resources the other answers are already giving a great overview. Use Karpathy's course for an intro to Deep Learning for CV but don't expect it to be comprehensive in terms of giving you an overview of CV. Learn OpenCV for more low level, non-ML and generally more "old-school" Computer Vision. A personal recommendation of mine is http://www.computervisionblog.com/ http://www.computervisionblog.com/ by Tomasz Malisiewicz. It's an excellent resource if you want to get an overview of what's happening in the field.

AndrewKemendo 9y ago
Great points. I would argue Kinetic or Geometrical Computer Vision problems, things like Tracking, Mapping, Reconstruction, Depth Estimation are best suited for the classical approaches like VO, SFM/MVS, SIFT/SURF, HOG etc... and are a separate category of CV problems than object recognition/detection/segmentation - much more capable of being done with ML because dimensionality is reduced. But there's also a whole field of CV that doesn't require Machine Learning learning at all (although it can benefit from it in some cases). In fact, Machine Learning has made almost no progress on most of what you mention, specifically SLAM and Multi-View Stereo. It takes completely rethinking how those are done when they are approached from the Deep Learning perspective.
nojvek 9y ago
I absolutely love Adrian's blog. http://www.pyimagesearch.com/ http://www.pyimagesearch.com/ He has articles on solving actual problems with OpenCV, dlib and tensorflow. I subscribe to the blog and try to do some of the tutorials myself. Udacity is another great resource. Their self driving and robotics nanodegrees are great. I am on the same path as you trying to pivot my career from full stack engineer and add CV + ML skills to it. When we have decent robot hardware, I want to be the one programming them, not the one getting replaced by them.

AndrewKemendo 9y ago

Great points. I would argue Kinetic or Geometrical Computer Vision problems, things like Tracking, Mapping, Reconstruction, Depth Estimation are best suited for the classical approaches like VO, SFM/MVS, SIFT/SURF, HOG etc... and are a separate category of CV problems than object recognition/detection/segmentation - much more capable of being done with ML because dimensionality is reduced. But there's also a whole field of CV that doesn't require Machine Learning learning at all (although it can benefit from it in some cases). In fact, Machine Learning has made almost no progress on most of what you mention, specifically SLAM and Multi-View Stereo. It takes completely rethinking how those are done when they are approached from the Deep Learning perspective.

nojvek 9y ago

I absolutely love Adrian's blog. http://www.pyimagesearch.com/ http://www.pyimagesearch.com/ He has articles on solving actual problems with OpenCV, dlib and tensorflow. I subscribe to the blog and try to do some of the tutorials myself. Udacity is another great resource. Their self driving and robotics nanodegrees are great. I am on the same path as you trying to pivot my career from full stack engineer and add CV + ML skills to it. When we have decent robot hardware, I want to be the one programming them, not the one getting replaced by them.