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Code for CVPR15 paper Learning to Compare Image Patches via Convolutional Neural Networks
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This package allows researches to apply the described networks to match image patches and extract corresponding patches.
We tried to make the code
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Wang, Lijun等提供的卷积神经网络跟踪算法，详细内容可参见论文Visual Tracking with Fully Convolutional Networks，测试运行环境为64-bit Arch Linux OS-Wang, Lijun and other neural network convolution tracking algorithm provided detailed information can be found in the paper Visual Tracki
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VGG卷积神经网络练习，在matlab中使用vlfeat/matconvnet对图像进行去模糊的操作进而更好的识别图像(VGG Convolutional Neural Networks Practical)
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We develop a new edge detection algorithm, holistically-nested edge detection (HED), which performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automat
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基于卷积神经网络的图像超分辨率重建，不含训练程序，包含已训练好的model !(Test code for Super-Resolution Convolutional Neural Networks (SRCNN))
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