Independent Deep Learning Specialist

Core business is the development of Artificial Intelligence software for the automation of visual inspections using Deep Learning.

The company was founded in 2001. After a decade of developing "traditional" image processing software the focus shifted from 2012 onwards to Deep Learning.

After retiring as a professor in 2022 and focusing on my private company, the company's mission was revised to help clients integrate Deep Learning technology into their products by:

• Offer semi-automatic annotation to minimise the effort of annotating datasets.
• Training and optimising Deep Learning models using client datasets.
• Providing source code needed for inference (production mode) and help with integration into client software.
• Support for integrated Deep Learning solutions, such as re-training models on new or enhanced datasets.
• Consulting on image acquisition, such as camera choice, optics and illumination.

This allows clients to harness the power of the new Artificial Intelligence approach Deep Learning to enhance their products.

The benefits for clients are:

• Keep focus on their own core business.
• Shorter time to market.
• No need to invest in the specialist knowledge and competences of Deep Learning.
• No need to invest in powerful and expensive computers required to train Deep Learning models.
• Deep Learning support during the rollout and evolution of their products.
• Access to knowledge and competences from more than 25 years of experience in over 400 applied research projects involving Computer Vision and Artificial Intelligence.

In consultation and free of charge, potential clients can have one of their annotated datasets trained and evaluated on one of our Deep Learning models to assess the feasibility of integrating Deep Learning into their products.

As a researcher and professor, I have been involved in more than 400 research projects on automating visual inspections using Artificial Intelligence since 1996. These projects often resulted in a proof-of-concept or demonstrator. What struck me over the years is that many (SME) companies did not have enough in-house knowledge to develop the proof-of-concept into a successful product.

My company focuses on the (technological) steps that come after the proof-of-concept to bring the product to market and evolve it further. An example of an ongoing project is the Iris Colony Counter project.