Turn photos into 3D with Meta's SAM 3D, using SAM 2 masks and Gaussian splatting, so you can build assets quickly for ...

A study has found that the way medical images are prepared before analysis can have a significant impact on the performance of deep learning models.

A research team has developed a binocular multispectral stereo imaging (BMSI) system capable of capturing synchronized three-dimensional (3D) plant morphology and multispectral reflectance information ...

In computer vision applications, Cyberway's self-developed high-performance image recognition algorithms have been successfully deployed in visual inventory counting scenarios, enabling automated ...

Meta announced SAM Audio, a new AI model that isolates any specific sound from complex audio or video mixtures, extending the ...

"The era of error-prone quality control is over," said Keven, CEO of UnitX. "With FleX, we achieve optimal accuracy with the easiest deployment tools--minimize downtime, enable scalable deployment, ...

Abstract: In this paper, we present UISE, a unified image segmentation framework that achieves efficient performance across various segmentation tasks, eliminating the need for multiple specialized ...

Abstract: Semi-supervised learning methods based on the mean teacher model have achieved great success in the field of 3-D medical image segmentation. However, most of the existing methods provide ...

A research team developed a fully automated, drone-based phenotyping workflow that can measure key peanut canopy and ...

SAM segments objects in images and videos, even audio can be separated by prompt: The AI model is freely available.

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, making it more applicable in clinical settings. The algorithm outperformed ...