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Machine Vision

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.

1

Process

2

Algorithm

3

Decision

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.

Case Study

Machine Vision

AUTOMATIC PLANNING OF SOLAR SYSTEMS

Our client is a company from the solar sector, which offers the planning, consultation and installation of solar systems for private households. The aim of this project is to automate the planning process of solar systems.... read more

Blog Posts regarding Machine Vision

August 27th, 2019

Artificial intelligence (AI) and in particular computer vision promise to be valuable aids for diagnosing diseases based on medical... read more

May 24th, 2019

This post presents some key learnings from our work on identifying roofs on satellite images. Our aim was to develop a planing tool for the placement of solar panels on roofs. For this purpose... read more

July 15th, 2019

In the past five to ten years, hardly any topic has seen such a stellar rise in popularity as Deep Learning. Since 2009 the number of Deep Learning papers published per year has more than... read more

Use Cases in Machine Vision

Industry

Department

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.

Authorities need up-to-date information on the use of their land to be able to plan public infrastructure.

Runways are controlled by air traffic controllers observing the runways from the tower. As this task requires constant attention and the detection of security sensitive objects on the runway or in the air, Computer Vision can support air traffic controllers.

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.

Crop Classification is a highly relevant topic in agriculture for farmers, authorities and companies selling to farmers alike.

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.

Construction plans are complex and include a high amount of relevant data. This information, however, is not standardized and available in a structured format. Therefore, data cannot be analyzed accurately and construction projects cannot be compared on a profound basis.

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.

In high-growth cities (mainly in Asia and Africa), it becomes a major challenge for urban planners to keep track of settlements and infrastructure need.

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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