The idea of semantic segmentation is to recognize and understand what is in an image at the pixel-level.
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Inspired by the selective attention in the visual cortex, artificial attention is designed to focus a neural network on the most task-relevant input signal.
The second goal is to learn instance information by estimating directional information of the instances' centers of mass densely for each voxel.
Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns.
Manual validation of a subset of these craters indicates that a majority of them are real, which we take as an indicator of the strength of our model in learning to identify craters, despite incomplete training data.
In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.
After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames.
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery.