Engineers have always tried to give the robot the gift of sight. So, they have to replicate the human vision process with computers, algorithms, cameras and more. In the DIY area, a Raspberry Pi is the queen of prototyping platforms. So, why not to use it in computer vision applications. The projects started coming fast and furious for navigation, localization, recognition, classifications, monitoring, reading and more.
"Computer vision" has a decidedly sci-fi ring, so it's no surprise the idea of smart devices becoming all-seeing tools has captured people's imaginations. Yet the true potential of the tech is held back by a widespread misunderstanding of what great applications look like and what they can achieve. Where did computer vision come from? It's the product of a proliferation of cheap, high-quality cameras, which has expanded the scope for imagery captured in public, private, and commercial domains. At the same time, advances in machine learning and deep learning technology are allowing us to transform those images into digital signals that support a wide range of actions.
Artificial intelligence and the application of it across nearly every aspect of our lives is shaping up to be one of the major step changes of our modern society. Today, a startup that wants to help other companies capitalise on AI's advances is announcing funding and emerging from stealth mode. Allegro.AI, which has built a deep learning platform that companies can use to build and train computer-vision-based technologies -- from self-driving car systems through to security, medical and any other services that require a system to read and parse visual data -- is today announcing that it has raised $11 million in funding, as it prepares for a full-scale launch of its commercial services later this year after running pilots and working with early users in a closed beta. The round may not be huge by today's startup standards, but the presence of strategic investors speaks to the interest that the startup has sparked and the gap in the market for what it is offering. It includes MizMaa Ventures -- a Chinese fund that is focused on investing in Israeli startups, along with participation from Robert Bosch Venture Capital GmbH (RBVC), Samsung Catalyst Fund and Israeli fund Dynamic Loop Capital.
Computer vision is fundamental for a broad set of Internet of Things (IoT) applications. Household monitoring systems use cameras to provide family members with a view of what's going on at home. Robots and drones use vision processing to map their environment and avoid obstacles in flight. Augmented reality glasses use computer vision to overlay important information on the user's view, and cars stitch images from multiple cameras mounted in the vehicle to provide drivers with a surround or "bird's eye" view which helps prevent collisions.
A new Israeli machine learning startup is throwing its hat in the computer vision ring with an algorithm that it says can beat the best on the market. Launching today, Brodmann17 touts new machine vision products that can help with pedestrian detection, face detection, and other tasks. One of the biggest problems in machine learning is getting advanced algorithms to run efficiently. It's one thing to train and run models on a power-guzzling server loaded with top of the line silicon and another to make them work on embedded processors in a smartphone, connected car, or camera. Brodmann17 claims that its algorithm is able to work on a wide variety of hardware and is faster, more accurate, and more efficient than other state of the art offerings.