Deep learning algorithms for object recognition are raising the computational bar once more, and that requires a new class of intelligent vision processors that can augment the processing efficiency ...

Despite this, the researchers ay the algorithm has performed as well or better in object recognition tests than other leading object recognition algorithms.

The BYU algorithm tested as well or better than other top object recognition algorithms to be published, including those developed by NYU's Rob Fergus and Thomas Serre of Brown University.

Researchers have shrunk state-of-the-art computer vision models to run on low-power devices. Growing pains: Visual recognition is deep learning’s strongest skill. Computer vision algorithms are ...

Object recognition for robots Robots' maps of their environments can make existing object-recognition algorithms more accurate Date: July 24, 2015 Source: Massachusetts Institute of Technology ...

It uses computer vision and image recognition to make its judgments. It may not seem impressive, after all a small child can tell you whether something is a hotdog or not.

A team of researchers at MIT CSAIL, in collaboration with Cornell University and Microsoft, have developed STEGO, an algorithm able to identify images down to the individual pixel.

AI image recognition has made some stunning advances, but as new research shows, the systems can still be tripped up by examples that would never fool a person. Labsix, a group of MIT students who ...

The process of identifying objects and understanding the world through the images collected from digital cameras is often referred to as 'computer vision' or 'machine vision.' ...

Computer vision algorithms usually rely on convolutional neural networks, or CNNs. CNNs typically use convolutional, pooling, ReLU, fully connected, and loss layers to simulate a visual cortex.