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Computer 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 Computer Vision projects

Criteria to discover attractive process automation projects, where visual information plays 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.

Our process

1. Process evaluation

Together we discuss your process automation projects along three different dimensions: cost savings, strategic value and technical feasibility. After settling for a specific project, we put special emphasis on the needs of the end users.

2. Innovative solutions

We are an experienced team of machine learners. Our algorithms find complicated patterns in unstructured, mostly visual and text data. Once detected, these patterns are the basis for the automation of the underlying process.

3. Decision-support software

We make a point of integrating our customers in the project's code repository as well as in weekly progress meetings. Agility, clean code and a modular program structure help us to deliver easy-to-maintain software, that simply works.

Projects in Computer Vision

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Automatic Planning of Solar Systems

Creative solutions enabled us to automate the process of planning solar systems

Artisanal and Small Mine Detection

See how machine learning can be used to stop environmental destruction

Defect Detection in Manufacturing

AI-supported optical defect detection for semiconductor laser production

Convective Clouds Detection

We automated the detection of certain cloud structures for Deutscher Wetterdienst (DWD)

Crop Type Classification

Predict crop types from satellite data to support modern agriculture

Monitoring Urban Growth and Change

An image segmentation algorithm that supports sustainable city planning

Predicting Potential Reach of Video Ad Campaigns

We simulate internet traffic and bidding scenarios to predict the reach of advertising campaigns.

Smart Access Control with Facial Recognition

We developed a multi-level security system with facial recognition for automatic access control.

Blog Posts in Computer Vision

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Computer Vision

An Introduction to Metric Learning

By William Clemens (PhD) September 26th, 2022

This blog post gives a brief introduction to metric learning. It explains common distance measures and loss functions such as triplet loss.

Introductions

Recommendation systems - an overview

By Konrad Mundinger August 29th, 2022

In this blog post, I will give an overview of the underlying basic concepts, common use cases and limitations of recommendation systems. Among other topics, I will discuss content-based and collaborative filtering.

Computer Vision

The best (Python) tools for remote sensing

By Emilius Richter August 2nd, 2022

I present the best (Python) tools for remote sensing and processing of satellite data, based on our practical experience with them at dida. For some I provide application examples including code.

Computer Vision

Image Captioning with Attention

By Madina Kasymova May 31st, 2022

In this article, we examine how an image caption generation pipeline works. In particular, we look at the attention mechanism - a very promising approach to image captioning.

Computer Vision

How to implement a labeling tool for image classification in a Jupyter notebook

By Felix Brunner March 21st, 2022

I will walk you step-by-step through creating a simple annotation tool to easily assign class labels to a set of images. The tool can be written and used within a Jupyter Notebook, making use of ipywidgets.

Computer Vision

Data-centric Machine Learning: Making customized ML solutions production-ready

By David Berscheid October 6th, 2021

In this article, we will see why many ML Projects do not make it into production, introduce the concepts of model- and data-centric ML, and give examples how we at dida improve projects by applying data-centric techniques.

Computer Vision

Classification of Crop Fields through Satellite Image Time Series

By Tiago Sanona August 19th, 2021

Following the paper by Garnot et al., we utilize an altered version of the Transformer to classify crop fields from time series of satellite images. With this, we achieve better results than traditional methods (e. g. random forests) and with less resources than recurrent networks.

Computer Vision

Extracting information from technical drawings

By Frank Weilandt (PhD) July 16th, 2021

In this blogpost we show how both traditional and deep learning-based computer vision techniques can be applied for information extraction from exploded-view technical drawings.

Use Cases in Computer Vision

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Webinars in Computer Vision

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