Recognizing Wood Knot Images

Bob Horton and Vanja Paunic walk through a lumber grading scenario with Microsoft R Server:

Here we use the rxFeaturize function from Microsoft R Server, which allows us to perform a number of transformations on the knot images in order to produce numerical features. We first resize the images to fit the dimensions required by the pre-trained deep neural model we will use, then extract the pixels to form a numerical data set, then run that data set through a DNN pre-trained model. The result of the image featurization is a numeric vector (“feature vector”) that represents key characteristics of that image.

Image featurization here is accomplished by using a deep neural network (DNN) model that has already been pre-trained by using millions of images. Currently, MRS supports four types of DNNs – three ResNet models (18, 50, 101)[1] and AlexNet [8].

This is a practical example of how to use image recognition to facilitate machine learning.

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