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Vision Systems for Deep Learning

Algorithms for artificial intelligence are improving rapidly, especially in the domain of artificial neural networks (ANNs). In the Medical & Life Sciences fields, in particular, many classification problems that were once considered to be “nonsolvable” by machines can now be solved with an impressive level of accuracy and robustness. While algorithm development has progressed quickly, accelerated by huge companies such as Google and Facebook, the implementation and deployment of ANNs remains a challenging problem for vision system developers worldwide.

Webinar Description:

This webinar will give you an overview of three different types of vision systems that can be used to deploy a trained neural network in the Medical & Life Sciences fields. A vision system consists of a camera, a data interface, and a processing unit. The three system architectures are:

  • Embedded,
  • PC-based, and
  • FPGA-based.

The webinar will discuss how these system architectures differ in their total cost of ownership, in the engineering effort required to deploy them, and in overall system performance.

Presented by:

After graduating in industrial engineering, Felix Chemnitz worked for several years as account manager for a leading German engineering service provider. In this role, he supported clients from all areas of industry in personnel selection and project management. He joined Basler in 2015, first in Business Development then in Area Sales Management. The detailed insights he gained into technical sales during this time help him in his current role as Product Market Manager Medical. He perfectly understands the needs of customers and translates them into the Medical & Life Sciences portfolio of Basler AG.

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