Machine Vision

Due to advances in the field of machine learning in recent years, any pattern in image data visible to the human eye can also be made visible to a machine. Sometimes machines can even be trained to detect structures not visible to humans.







Criteria to discover attractive process automation projects, where visual information play a crucial role:

  • Currently the process is cost-intensive and/or a faster decision creates substantial value
  • A (trained) human could make a good decision mainly based on visual information
  • There is enough data available (as a rule of thumb: 200 - 2.000 images. This, of course, is highly dependent on the use-case)

In our experience, only by combining knowhow of internal operations with machine vision expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.

Use Cases in Machine Vision

Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.

Diagnostic suggestions can be made by analyzing X-ray or MRI images using an algorithm.

Through standardized images of different leathers, a machine learning algorithm can classify leather and assign it to the right garments.

Photos of defective spare parts are analyzed by a machine learning algorithm and the damage is classified. This information is used in the development of new products and in the selection of suppliers.

The optimum price of a building insurance can be determined by the customer specifying the address of a building, for example, and any existing pictures of the customer.

Defective components can cause major production losses. To prevent this, an algorithm can recognize patterns and distinguish faulty parts from faultless ones at an early stage.

Neural networks are used to detect and filter out patterns of fraud cases. Conspicuous damage reports are reported to claim handlers and checked manually.

A machine learning algorithm analyses the properties and historical and current data of a property. This results in a score by which the attractiveness of an object can be evaluated.

A photo of an object is taken to simplify the process of creating the advertisement. This picture gets analysed and the finished advertisement is displayed to the customer.

An algorithm can analyze thousands of influencer styles and outfits and predict next season's trends.

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