DURHAM, N.C. -- The artificial intelligence behind self-driving cars, medical image analysis and other computer vision applications relies on what's called deep neural networks. Loosely modeled on the ...

Recent advancements have witnessed an impressive convergence between neural network architectures and spectroscopic techniques within computer vision. Deep learning methods, particularly convolutional ...

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.

Computer vision is one of the hottest areas of computer science and artificial intelligence research, but it can't yet compete with the power of the human eye. Here's why. Our team tests, rates, and ...

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 ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

Computer vision, or the ability of artificially intelligent systems to “see” like humans, has been a subject of increasing interest and rigorous research for decades now. As a way of emulating the ...

In recent years, owing to the advancements in the immense processing ability and parallelism of modern graphics processing units (GPUs), deep learning based on convolutional neural networks (CNN) has ...

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 ...

Ultralytics Inc., a developer of computer vision models, today announced that it has raised $30 million in funding. Elephant VC led the Series A round with participation from SquareOne. Ultralytics ...

New research offers clues to what goes on inside the minds of machines as they learn to see. Instead of attempting to account for a neural network's decision-making on a post hoc basis, their method ...