Abstract: The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed ...
Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is an important task in remote sensing. However, due to its complex scattering mechanism and high-dimensional features, ...
Abstract: Malware classification m ethods a re o ften costly, requiring constant retraining and large amounts of computing power in order to support large models that analyze software in numerous ways ...
Abstract: Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on ...
Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
Abstract: Hyperspectral image classification methods based on subgraph neural networks (SGNNs) are rarely explored, and its advantage is that it can alleviate the neighbor explosion problem. After ...
Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
Add a description, image, and links to the tensorflow-keras-cnn-image-classification topic page so that developers can more easily learn about it.
Photoshop CC tutorial showing how to quickly transform an ordinary color or black & white photo into a dramatically, compelling image. This works especially well with landscapes and seascapes.
Abstract: Gas detection is essential in industrial and domestic environments to ensure safety and prevent hazardous incidents. Traditional single-sensor time-series analysis often suffers from ...
Abstract: In the reversible data hiding (RDH) community, there are already many depth predictors for grayscale images, but depth predictors for color images are urgently needed to be developed.
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