Adaptive Vision Studio 4.9 is data-flow based software designed for machine vision engineers. It does not require any programming skills, but it is still so powerful that it can win even with solutions based on low-level programming libraries. And this has been proved with some of the most interesting vision systems in the world. What is more, the architecture is highly flexible, ensuring that users can easily adapt the product to the way they work and to specific requirements of any project. Its unique strength lies in its focus on professional users – it allows you to create typical applications easily, but at the same time makes it possible to efficiently develop highly customized and large-scale projects.

Adaptive Vision Library is a machine vision library for C++ and .NET programmers. It provides a comprehensive set of functions for creating industrial image analysis applications - from standard-based image acquisition interfaces, through low-level image processing routines, to ready-made tools such as template matching, measurements or barcode readers. The main strengths of the product include the highest performance, modern design and simple structure making it easy to integrate with the rest of your code.

The functions available in Adaptive Vision Library closely correspond to the filters of Adaptive Vision Studio. Therefore, it is possible to prototype your algorithms quickly in a graphical environment and then translate them to C++ or .NET, or even generate the C++ code automatically..

Adaptive Vision Library gives you instant access to highest quality, well optimized and field-tested code that you need for your machine vision projects!

Deep Learning Add-on is a new breakthrough in machine vision applications. It is a set of ready-made tools which are trained with Good and Bad samples, and which then detect defects or features automatically. Internally it uses large neural network structures, designed and optimized by our research team for use in industrial inspection systems. For the user, however, they are provided as simple filters with very few parameters, and with easy-to-use graphical tools for convenient execution of the training process.

In Deep Learning, as in all fields of machine learning, it is very important to follow correct methodology. The most important rule is to separate the Training set from the Validation set. The Training set is a set of samples used for creating a model. We cannot use it to measure the model's performace, as this often generates result that are overoptimistic. Thus, we use separate data - the Validation set - to evaluate the model.