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 ...

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it ...

Computer vision continues to be one of the most dynamic and impactful fields in artificial intelligence. Thanks to breakthroughs in deep learning, architecture design and data efficiency, machines are ...

What if you could teach a computer to recognize a zebra without ever showing it one? Imagine a world where object detection isn’t bound by the limits of endless training data or high-powered hardware.

Selecting the right edge device for real-time AI-powered vision is a critical decision that can impact the performance, usability, and versatility of your applications. This comparison between the ...

What are convolutional neural networks in deep learning? Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.

Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ...

Many of today's businesses have recognized the benefits of AI. McKinsey reports that computer vision ranks second among all other AI solutions in terms of application, and Statista research predicts ...