Data augmentation for Images
It’s usual that big, well-documented, and reliable datasets for training and testing some Machine Learning models are often hard to find.
Computer Vision/Machine Learning Specialist
It’s usual that big, well-documented, and reliable datasets for training and testing some Machine Learning models are often hard to find.
When trying to segment desired regions of an image, sometimes we need more than one algorithm. K-means is very often one of them.
Everyone that uses OpenCV is familiar with cv::Mat. Although some developers never heard about UMat class and its advantages.
Last week I had an exciting opportunity: implement a camera prototype using the Raspberry Pi.
A new version of OpenCV has been released so… Time to update!
Last month I made a tutorial on how to build and install OpenCV 3.2 on Windows 10 using CMake and MinGW on Windows. Since then, I received some requests asking for a Linux equivalent one. So, here is the step-by-step.
Recently I decided to update my OpenCV from 2.4 up to 3.2. I went through problems, and errors since am coding with C++ in Eclipse and using MinGW on Windows 10. After a little struggle, I finally got it running. Here is the step by step I used.