Image segmentation continues to represent a cornerstone of computer vision, underpinning applications from medical diagnostics to industrial automation.

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 ...

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.

Our multidisciplinary team of 50+ algorithm experts, computer science engineers, physicists, radiologists, echo specialists and in-house medical annotation teams, continue to provide innovative ...

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors.

“While the performance of the algorithms in image data evaluation partly depends indeed on the quantity and quality of the data, the algorithm design is another crucial factor, for example with regard ...

Catalog : COMP.4230 Computer Vision I (Formerly 91.423 & 91.523) Id: 038743 Credits Min: 3 Credits Max: 3 Description Computer vision has seen remarkable progress in the last decade, fueled by the ...

Examples of computer vision in action include optical character recognition, image recognition, pattern recognition, facial recognition, and object detection and classification.

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.