31–39 of 39 posts
Check out this beard length detector I wrote in 22 lines of Python with SimpleCV http://labs.radiantmachines.com/beard/
It's difficult to effectively use cv libraries (such as opencv) because in order to approach new problems, understanding of the underlying theory and methodology is often necessary. Example code can only go so far to teach the user how to evaluate and adapt to new constraints. simplecv.org is down for me right now, but I hope that it addresses this concern! The principles of computer vision are not too difficult to g…
http://szeliski.org/Book/ http://www.cse.usf.edu/~r1k/MachineVisionBook/
It's difficult to effectively use cv libraries (such as opencv) because in order to approach new problems, understanding of the underlying theory and methodology is often necessary. Example code can only go so far to teach the user how to evaluate and adapt to new constraints. simplecv.org is down for me right now, but I hope that it addresses this concern! The principles of computer vision are not too difficult to g…
Unfortunately computer vision libraries and toolsets are the worst offenders of making each algorithm a 'hammer' when traditional problems are not a 'nail'. A good example is "Detecting a Car" ( http://tutorial.simplecv.org/en/latest/examples/parking.html ), where the mean color is used to determine if a car is in the image. A nice little exercise, but it oversimplifies the process. Each allows the user to step in a…
True object recognition would require strong AI. Every real life vision system is a compromise that makes many assumptions about its input. You'd be foolish to reach for something like SIFT if you really know that the car is always yellow and it always parks in the same 200x200px square.
It's difficult to effectively use cv libraries (such as opencv) because in order to approach new problems, understanding of the underlying theory and methodology is often necessary. Example code can only go so far to teach the user how to evaluate and adapt to new constraints. simplecv.org is down for me right now, but I hope that it addresses this concern! The principles of computer vision are not too difficult to g…
OpenCV, simpleCV etc are very useful libraries for toying around with images. You can get interesting results without a lot of effort. But the more serious you get about image recognition, the more you find that you can't use them globally across the image. Finding the yellow car in the parking spot is a good example of the usefulness of the library and also its simplistic capabilities. It recognized a yellow patch in the image and it doesn't recognize a car in a general way. When you're ready to write code to recognize a car, you'll probably find you can't use openCV libraries.
What has worked amazingly well for me, is to create a model of the image areas and then apply transformations based on the model. If you have a sharp foreground and a blurry background, don't run the recognition algorithms that rely on sharp edges on the blurry background.
Earlier quoted context omitted.
I'm looking to measure dimensions of objects using computer vision libraries. I think it is impossible to do this without a reference scale in the same image. What is the unit of measure for line.length()?
For my use it is just arbitrary. SimpleCV does have the ability to calibrate cameras, which should give you what you want.
Unfortunately computer vision libraries and toolsets are the worst offenders of making each algorithm a 'hammer' when traditional problems are not a 'nail'. A good example is "Detecting a Car" ( http://tutorial.simplecv.org/en/latest/examples/parking.html ), where the mean color is used to determine if a car is in the image. A nice little exercise, but it oversimplifies the process. Each allows the user to step in a…
Isn't the most important part of any algorithm whether it will fit the purpose? For the situation they describe (purely as an example) their algorithm seems it will probably work quite well. You're not going to build a humanoid robot using that algorithm, but it is addressing the problem they specified so directly that anything more would be over-engineering the solution. Most real-life problems are going to be addre…
Clearly.
> Most real-life problems are going to be addressable using similar techniques.
Nope.