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U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [ 2 ] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation .
U-Net is a convolutional neural network which takes as input an image and outputs a label for each pixel. [83] U-Net initially was developed to detect cell boundaries in biomedical images. U-Net follows classical autoencoder architecture, as such it contains two sub-structures. The encoder structure follows the traditional stack of ...
Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500) 500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300. Each image segmented by five different subjects on average. 500 Segmented images Contour detection and hierarchical image segmentation 2011 [11]
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Popular approaches are U-Net architectures to predict voxel-wise affinities paired with a watershed segmentation [7] and flood-filling networks. [8] These approaches produce an over-segmentation which can be manually or automatically agglomerated to correctly represent a neuron.
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The typical network architectures used to remove these sparse sampling artifacts are U-net [10] [12] and Fully Dense (FD) U-net. [11] Both of these architectures contain a compression and decompression phase. The compression phase learns to compress the image to a latent representation that lacks the imaging artifacts and other details. [23]
Can we imagine ourselves back on that awful day in the summer of 2010, in the hot firefight that went on for nine hours? Men frenzied with exhaustion and reckless exuberance, eyes and throats burning from dust and smoke, in a battle that erupted after Taliban insurgents castrated a young boy in the village, knowing his family would summon nearby Marines for help and the Marines would come ...