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ENHANCED CONVOLUTIONAL NEURAL NETWORK FOR IMAGE SEGMENTATION

机译:增强的卷积神经网络进行图像分割

摘要

This disclosure relates to digital image segmentation and region of interest identification. A computer implemented image segmentation method and system are particularly disclosed, including a predictive model trained based on a deep fully convolutional neural network. The model is trained using a loss function in at least one intermediate layer in addition to a loss function at the final stage of the full convolutional neural network. The predictive segmentation model trained in such a manner requires less training parameters and facilitates quicker and more accurate identification of relevant local and global features in the input image. In one implementation, the fully convolutional neural network is further supplemented with a conditional adversarial neural networks iteratively trained with the fully convolutional neural network as a discriminator measuring the quality of the predictive model generated by the fully convolutional neural network.
机译:本公开涉及数字图像分割和关注区域识别。特别公开了一种计算机实现的图像分割方法和系统,包括基于深度全卷积神经网络训练的预测模型。除了在全卷积神经网络的最后阶段使用损失函数之外,还使用至少一个中间层中的损失函数来训练模型。以这种方式训练的预测性分割模型需要较少的训练参数,并有助于更快,更准确地识别输入图像中的相关局部和全局特征。在一个实现中,全卷积神经网络进一步补充有条件对抗神经网络,该条件对抗神经网络用全卷积神经网络作为鉴别器来迭代训练,该条件鉴别器测量由全卷积神经网络生成的预测模型的质量。

著录项

  • 公开/公告号WO2019194865A1

    专利类型

  • 公开/公告日2019-10-10

    原文格式PDF

  • 申请/专利权人 12 SIGMA TECHNOLOGIES;

    申请/专利号WO2018US57529

  • 申请日2018-10-25

  • 分类号G06K9/46;G06K9/66;G06T7/11;G06T7/143;G06N3/04;G06N5/04;G06F15/18;

  • 国家 WO

  • 入库时间 2022-08-21 11:52:55

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