Category: Machine Learning

OpenCV & ML (Deep Learning) 04 – Classification and Mulit-layer Perceptron (MLP)

Employing OpenCV’sMulti-layer Perceptron from Artificial Neural Network module (ANN_MLP) for classifying various classes of samples drawn from multi variant normal distribution with different man and co-variance matrices. The example for 2-dimension samples are visualized in OpenCV and for upper dimensions bench-marked against Keras (with TensorFlow backend) results. In the previous tutorial we modeled a simple

OpenCV & ML (Deep Learning) 03 – Regression and Mulit-layer Perceptron (MLP)

Utilizing OpenCV’s Multi-layer Perceptron from Artificial Neural Network module (ANN_MLP) for predicting a periodic or parabola 1-dimensional function (A regression problem). Samples are drawn randomly within a specific range in function domain meanwhile a rectangular (uniform distributed) or Gaussian (normal distributed) noise are applied to them. Now, we could step forward and practice a much

OpenCV & ML (Deep Learning) 01 – Building library from Source

Building OpenCV 3.3 library via source codes cloned from github.com. The library is build with contribute modules of OpenCV. CMake is used for configuring the build options. The default install target is modified in order to provide the possibility of  using different build configurations in one system. The CUDA and OpenNI modules are skipped among

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