We hope, like us, you take image processing seriously. You may even have a number of suggestions about image processing and have some relevant experience. If you have used Matlab, you might dislike the unexpected change from its relative simplicity to C/C. Today we’re presenting a few of the reasons why computer vision with OpenCV is better than Matlab.
OpenCV was created for image processing. All of the information and features of the system were fashioned with the Image Processing coder in mind. Matlab, on the other hand, is pretty generic. You receive just about everything in Matlab in the form of toolboxes. This could be anything from financial toolboxes to highly specific DNA toolboxes.
Matlab is simply far too slow. Matlab is built on the Java platform. And Java is built on C. When you use Matlab on your personal computer, the processor takes far longer to decode the Java language that it makes for a slow user experience.
According to our testing, a maximum of approximately 4-5 frames is being processed per second. With OpenCV, actual real-time processing is at around thirty frames per second.
Sure you pay the cost for speed — a far more cryptic language to cope with, but it’s certainly worthwhile. You are able to do lots more… you could do truly complex mathematics on pictures with C and still escape with adequate speeds for the program of yours.
Matlab just simply uses an excessive amount of system resources. With OpenCV, you are able to escape with as few as 10mb RAM for a real-time application. But with modern computers, the RAM element is not a huge item to be concerned about. You do have to be careful about memory leaks, though it is not that difficult. Check out this post about Memory Management in OpenCV as an example.
If you’re able to get your application programs to operate on a ten-year-old computer, you are a genius! These are just some of the reasons why we think OpenCV is a better option than Matlab.