Object detection and recognition are an integral part of computer vision systems. In computer vision, the work begins with a breakdown of the scene into components that a computer can see and analyse.

The object detection required for machine vision applications such as autonomous driving, smart manufacturing, and surveillance applications depends on AI modeling. The goal now is to improve the ...

Vision is a powerful human sensory input. It enables complex tasks and processes we take for granted. With an increase in AoT™ (Autonomy of Things) in diverse applications ranging from transportation ...

When searching for basketball videos online, a long list of Web sites appears, which may contain a picture or a word describing a basketball. But what if the computer could search inside videos for a ...

In 2015, the launch of YOLO — a high-performing computer vision model that could produce predictions for real-time object detection — started an avalanche of progress that sped up computer vision’s ...

Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying to offer the best of simplicity and ...

Computer vision trains AI to interpret images, automating tasks like driving and product tracking. Applications include Amazon's "Just Walk Out" tech and autonomous vehicles' navigation systems. Uses ...

Indoor farms, also known as vertical farms, are popular among agricultural researchers and are expanding across the agricultural industry. Some benefits they have over outdoor farms include the ...