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.

Related Posts

The Lesser-Known Apply Functions In R

Andrew Treadway covers a few of the lesser-known apply functions in R: rapply Let’s start with rapply. This function has a couple of different purposes. One is to recursively apply a function to a list. We’ll get to that in a moment. The other use of rapply is to a apply a function to only those elements in […]

Read More

Controlling Azure Services In R With AzureR

Hong Ooi announces a new set of packages called AzureR: As background, some of you may remember the AzureSMR package, which was written a few years back as an R interface to Azure. AzureSMR was very successful and gained a significant number of users, but it was never meant to be maintainable in the long term. As […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930