Image segmentation, the process of identifying objects from an image, has been around for as long as digital images. Usually, this process involves creating and using algorithms to classify pixels in an image and then using this classification to identify objects. Various algorithms exist to classify pixels, which often work fast and well but have several limitations regarding the types of images that can be analyzed, and often require a high level of knowledge and skill to apply successfully. whereas humans can usually be trained quickly to recognise objects from images. Deep Learning works by using human knowledge of what constitutes an object to train a model that a computer can use to interpret the image data and produce objects. The exact workings of how a DL network performs the classification are highly complex, but the process of creating a network and applying it to images is accessible to anyone with basic computer skills.

Since arivis Vision4D 3.6 it has been possible to run DL inference for segmentation. This inference can use pre-trained models created with apeer and other platforms that use ONNX models. This only requires that the Vision4D license include the Analysis module.

With Vision4D/arivis Pro 4.1 it is possible to train models directly in the application. This requires a license that includes the AI toolkit and also requires that the arivis Deep Learning package for GPU acceleration be installed.