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Execute perClass classifiers in Cognex Vision Pro

In this video we show how to execute perClass classifiers from Cognex Vision Pro. We will load a simple dice classifier trained in Matlab, connect its input and output buffers and then run it to any new image within the Vision Pro. The simple perClass .Net API allows you to do this in few minutes
Interested to add classification capabilities to your Cognex application? Contact us!

Understanding the data with interactive scatter

Interactive scatter plot helps us to gain understanding of our problem. This tutorial provides a walkthrough example on medical data set. You will learn how to use filters, label subsets of data and identify differences in class distributions.

Local image features

Before we can experiment with classifiers we need to find a good data representation for our images. We may want to extract features that highlight relevant characteristics for our application. Very often we need to learn from local texture information, as we can leverage the spatial connectivity of the pixels. In this tutorial we will focus on extracting local image features and illustrate why it is important.

Detectors explained

A detector is a classifier that focuses only on one class of interest. It can be very handy especially when we have lot of samples of one specific target class and do not know much about the other classes. In this tutorial you will learn how to construct a detector in one-class scenario or when non-targets are also present.

Identify typical and rare samples

When we submit a new sample to a classifier, we learn the class to which the sample is assigned. What if we would like to be sure of our classifier decision. Does the classifier have a strong evidence that the sample belongs to the class? This video shows how to identify if a sample is a typical or rare example of its class.

Ground-truth by clustering

How to gather a labelled training set from images? You could paint some relevant areas manually. But this is tedious and not friendly for your wrist. This video shows how clustering may provide a better alternative.

Choose the classifier with cross-validation

How to reliably estimate the performance of a classifier? The cross-validation is a good tool for it. It can also be used to simulate a realistic classifier operation.

Tune your classifier

Not all errors are equally important. In sorting applications, some errors should be entirely avoided. In this tutorial, you will learn how to tune your classifier to find all rotten parts in French fries sorting application.

Learn from local texture

Learning from local texture is a must in several applications such as quality control or medical diagnostic. In this video we will learn how to build classifiers based on color and texture in a rock sorting application.

Why meta-data matters?

What information is useful when designing your classifier? This video shows how meta-data helps you to improve not only the classifier performances, but also your understanding.

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