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The project applies clustering and dimensionality reduction for image segmentation on a resized images (reduced to 50% size). The image data, originally in 3D RGB format, was converted to a 2D pixel ...

Tiarna Lee explores the root causes of racial bias in AI-driven cardiac imaging to support the development of fairer, more ...

No image is infinitely sharp. For 150 years, it has been known that no matter how ingeniously you build a microscope or a ...

CONTAIN™ represents the next breakthrough in AI-powered ore sorting from TOMRA Mining – a deep learning solution purpose-built to classify complex inclusion-type ores with unprecedented accuracy. By ...

The conversion of forests to cropland, particularly for small-scale agriculture, has steadily increased over the past two ...

To sort through them, Pratheek Rebala, a computational journalist, developed a custom image-recognition algorithm. He fed land titles and land registers the team had already identified as training ...

Abstract: Graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with ...

With the development of convolutional neural networks, there are more and more different remote sensing image classification algorithms. In order to improve the accuracy of remote sensing image ...