Mosaic Data Science is building easily deployed custom AI applications powered by deep learning to automate tasks that normally require human vision.
Computer Vision | AI within reach
Computer vision is a field of artificial intelligence that trains a computer to extract the kind of information from images that would normally require human vision.
The goal of these deep learning models is not only to see, but also process and provide useful results based on the observation.
Machines can accurately classify and summarize images, identify objects, find similar images, and more. With the right application, they can react to what they see and automate a traditionally time-consuming process.
New to Computer Vision & Deep Learning? Don’t worry, Mosaic has you covered in our Intro to Computer Vision Blog Post
Neural what? Our intro to computer vision blog provides a gentle introduction to this deep learning technique. Mosaic focuses on the explainability of AI & machine learning, we can unpack any black box.
Businesses are adopting machine vision, yet many experience challenges
According to the leading market research firm Statista – the computer vision market is expected to grow to $30B in annual revenue by 2025.
Based on the latest media hype, you might think a business can just point convolutional neural networks at a problem, and presto, they have machines classifying images. In reality, properly designing and deploying a functional computer vision system involves complex steps that require significant expertise to carry out successfully.
It is very challenging for a single piece of software to solve multiple use cases. Each organization’s data is quite different. A Fast R-CNN might fit a set of data inputs for one use case, while a SSD is better at identifying a different set of images. If you are going for competitive advantage, why not build something powerful & unique to your business?
Without understanding the mechanics behind these models, you run the risk of not understanding the outputs, not being able to tune the model or not being able to translate analytics to business terms. You’ll spend a lot of time and money on a failed project if you don’t rely on the experts.
Many companies have failed to properly use computer vision because they lack the required experience. Experts with significant computer vision experience can help you create models that are adaptive and customizable, with understandable outputs and analytics that translate to business value.
Mosaic helps organizations in every major industry identify processes that are ripe for the automation benefits from computer vision. In the following illustration, we examine a few success stories where we design & deploy custom solutions powered by this technology.
Mosaic can build you a powerful, custom computer vision solution. Our data scientists are experts at the deep learning development needed to train these machines. Our data engineers can help gather and organize your data. Our software engineers can integrate your computer vision models with products and deploy them in a scalable infrastructure.
Buying an out-of-the-box solution might sound nice, but tuning deep learning takes substantial customization from an experienced data scientist. If a company promises you an out of the box solution to classify images, they do not understand the complexity of computer vision or artificial intelligence and you run the risk of a black box that doesn’t work.
Buy vs. Build: What’s the best approach for custom software?
Computer vision sounds expensive. At Mosaic, we use existing infrastructure and leverage open source platforms to achieve significant cost savings. Mosaic is a proponent of custom software, believing this approach truly provides businesses with a unique competitive advantage. Effective deep learning solutions must be customized to the specific customer domain and data. Off-the-shelf solutions simply can not provide the true benefits of computer vision.
Few Shot Learning
Object detection in video has become a matter of routine, however, expanding these models to detect an object of your choosing requires many thousands, if not tens of thousands, of training examples. Few shot learners seek to make this process cheaper and easier by learning to detect new objects with only a small handful of examples (i.e. 1-30).
Published Paper Vision-Based Navigation for Airfield Surface Operation
This paper presents a vision-based navigation solution for unmanned aircraft operations on airfield surfaces in GPS-denied environments. The solution combines measurements from a computer vision system and inertial sensors with an airport layout database to provide accurate real-time position determination on the airfield surface.
Mosaic compiled a sheet examining the rise of AI vision.
Computer Vision in 3 Steps
Finding an Image
Ingest images from various sources in real-time to be stored for analysis.
Data scientists train these deep learning models to identify thousands of labeled images. After training/validation automation can be easily deployed.
Understand the Outputs
After the artificial intelligence classifies the object, system provides outputs back to the user or another machine.