Image Analysis Platform (IAP)
We provide image recognition software and cloud infrastructure for data analysis and storage.
Our platform is used by hundreds of customers worldwide and is also available for partners to host or integrate their own image analysis pipeline. This can be done via our API for existing imaging solutions or with a new custom solution.
Are you wondering how your business could benefit from artificial intelligence and automated image analysis? Did you know that there is no need to start from scratch if you have a solution that works already? Our customized image recognition software can be fully integrated into your existing software due to the open nature of our API.
With our Image Analysis Platform, you can save hundreds of hours of work that would have gone into manual labor. Image analysis automation provides you with accurate results and secure data management in less time, so you can save your resources for something that brings more value to your business.
Let artificial intelligence do the hard work for you. Start saving time and costs now!
“We are glad to have found a partner like Oculyze, who, together with us, have taken the AI image recognition of our new development to a new level at an incredible speed. The implementation went smoothly and was always characterized by progress. We are very much looking forward to further cooperation and future projects with the Oculyze team.”
CEO – 4BioCell GmbH
“We challenged Oculyze with the ambitious goal to convert their AI into a tool for beetle identification. We had no previous experience with AI-development and when it comes to entomology, neither did they. However, the cooperation was great and they drove the development constantly forward. We quickly got a useful application that is able to automate a process, which normally would require a lot of expert’s knowledge.”
Dr. Björn Lutsch
Customized image analysis services
Step #1 – Take your images and send them to the Oculyze cloud
Step #2 – Our customized image recognition software analyzes your images
Step #3 – Your results are sent directly to you, giving you access from any internet-enabled device or get direct access to our API and create your own interface.
Step #4 – Generate customized reports, view historical data, track your results over time and increase statistical accuracy
Step #5 – Get increasingly better software as the image database grows
Learn more about our customized solutions
Let us help you digitalize the tedious and error prone manual analysis processes. Our image analysis technology combines methodical pattern recognition with artificial intelligence and deep learning, allowing us to develop more robust systems with less data than with deep learning alone.
Why it makes sense to run your AI built computer vision software in the cloud
AI Computer Vision Software
Using machine learning and Artificial Intelligence (AI), Oculyze has transferred the computer vision software for lab equipment from the table top to the cloud. We automate expert image analysis combining methodical pattern recognition with artificial intelligence (AI) and deep learning to create some of the best computer vision software money can buy. This base technology, used in the Oculyze yeast cell counters, Better Brewing and Fermentation Wine, has convinced hundreds of yeast labs, breweries and wineries of all sizes around the world.
By AI computer vision software we mean software that is able to do useful things, but without all the instructions being hard coded. Traditional software needs all possible cases to be taken into consideration during the initial programming. Since we mostly automate the analysis of images with a lot of variance, noise and biological diversity this feature comes in very handy as the software performs great on scenarios none of us has ever seen before and does so consistently.
Our first algorithms were specifically trained to count yeast cells in very challenging situations (high concentrations, in clusters and mixed with other particles). By watching thousands of these images and counting the cells in them over and over again the algorithms learned what is a live cell, a dead cell and how many cells are actually in a specific cluster. The algorithms were helped by traditional image pre-processing taken from traditional pattern recognition techniques.
Critics of this so called narrow AI say that it is actually artificial experience (AE) and not intelligence. It is estimated that it took the deep learning network “five” the equivalent of 45.000 years to beat humans in the game Dota 2. While this shows how much “experience” went into the intelligence of this network it also makes it easy to understand why this type of software is so superior for many tasks. When our hardware was successfully validated by the @vlb in 2016, the algorithms had been in training for less than one human year, yet the system performed as well as a professional with 20 years of experience.
Hard coded image recognition devices have been around since the 1950’s and combined the worst of two worlds- they were as expensive as the yearly salary of a human expert and were not able to learn from the samples they analyzed. Unless their software is later manually re-programmed the software stays the same forever. The price for these devices has dropped significantly in the last decade but until recently they were not able to gain any experience and the software did not improve over time.
In the Cloud
This changed when we started to use the cloud for other things than streaming music and storing pictures. The cloud gave us the cost efficient ability to use extremely high computing power by proxy on affordable handheld devices (<200 EUR/USD). Our computationally demanding algorithms would not be possible to run locally in a practical timeframe, but because the calculations are being performed in the cloud you have it instantly available on a handheld device.
As a result
Real beauty happens when you combine the two components, AI and the cloud, and gain a system that is flexible and affordable, using the experience from the many for the benefit of each individual (user). The samples from the devices allow the algorithms in the cloud to keep learning and improving the computer vision software for all users. This is how Oculyze computer vision software keeps getting better and better.
Some claim that a AI network can’t apply what it learned while playing Chess to play Go, reinforcing the argument that it is actually artificial experience and not artificial intelligence. While that may be true for different games, we have noticed that the experience gathered while counting yeast is helping our algorithms to count fibers, cylinders and other shapes better and quicker. This allows us to dramatically reduce the amount of images we need in our image analysis platform to train the initial algorithm for new applications.
In a time of shortage of skilled workers it makes a lot of sense to automate visual analysis tasks and reduce the reliance on human experience in favor of an artificial intelligence that stays within the company. It does not take 45.000 years to teach a human how to perform a manual visual analysis but it is impossible for a human to gain or access the combined experience available to cloud based computer vision software.
Since you have all your data already saved in a standardized format in the cloud, it becomes possible to also use AI for more advanced interpretations of the data. For wineries for example we help predict problems with fermentation by combining the results of various measurements, thus allowing our customers to react earlier and avoid problems before they even occur.
In summary: Cloud based, AI trained computer vision software is faster to develop and deploy, cheaper, more accurate and keeps improving over time.