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.

Few-shot object detection addresses the challenge of recognising and localising novel object categories from only a handful of annotated examples. Traditional deep-learning detectors demand extensive ...

Digital systems are expected to navigate real-world environments, understand multimedia content, and make high-stakes ...

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 ...

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 ...

Computer vision could be a lot faster and better if we skip the concept of still frames and instead directly analyze the data stream from a camera. At least, that’s the theory that the newest ...

Medical image (MI) processing on the other hand involves much more detailed analysis of medical images that are typically grayscale such as MRI, CT, or X-ray images for automated pathology detection, ...

Scientists need to put more effort and resources into developing deep-learning forecasting models that consider the morphology of PV panels, a group of academics led by the University of Cambridge ...