HS deep image feature extraction becomes complex and time consuming due to the hundreds of spectral bands available in the hypercubes. Methods The proposed method aims condense the spectral-spatial information through suitable feature extraction and feature selection methods to reduce data dimension to ...
fast-turnover H3K4me3, slow-turnover H3K4me3, and persistent H3K4me3 (persisting in early 1-pachytene but excluding early-forming H3K4me3; Supplementary information, TableS5). A striking feature was that the average width around
Finally, the shallow texture information is adopted to compensate for the detail of the feature map to enhance the quality of the density map. Extensive experiments and comparisons on three challenge datasets, including ShanghaiTech Part_A & Part_B, UCF_CC_50, and UCF-QNRF, illustrate the ...
Marine object detectionFeature enrichmentFeature fusionConvolutional neural networkNeural Computing and Applications - Marine object detection has become increasingly important in intelligent underwater robot. Because of color cast and blur in underwater images, features directly......
Finally, the size of the output was (N, C2, T) and the output of m STCMs was the underlying feature of each VSG after spatial–temporal mining. 3.2. UDIM Architecture and the Implementation Process The UDIM goal was to identify the unstable DERs and predict the system stability, which is...
However, incomplete feature extraction, inappropriate feature fusion, and high time consumption are still the major problems for CNN applications in large-scale fine land cover mapping. In this study, a Spatial-Convolution Spectral-Transformer Interactive Network (SCSTIN) was proposed to integrate 2D-...