首页> 外国专利> Learning method and learning device for reducing distortion occurred in warped image generated in process of stabilizing jittered image by using GAN to enhance fault tolerance and fluctuation robustness in extreme situations

Learning method and learning device for reducing distortion occurred in warped image generated in process of stabilizing jittered image by using GAN to enhance fault tolerance and fluctuation robustness in extreme situations

机译:通过使用gan来增强在极端情况下的容错性和波动鲁棒性来减少在稳定抖动图像的过程中生成的变形图像中发生的畸变的学习方法和学习装置

摘要

A method for learning reduction of distortion occurred in a warped image by using a GAN is provided for enhancing fault tolerance and fluctuation robustness in extreme situations. And the method includes steps of: (a) if an initial image is acquired, instructing an adjusting layer included in the generating network to adjust at least part of initial feature values, to thereby transform the initial image into an adjusted image; and (b) if at least part of (i) a naturality score, (ii) a maintenance score, and (iii) a similarity score are acquired, instructing a loss layer included in the generating network to generate a generating network loss by referring to said at least part of the naturality score, the maintenance score and the similarity score, and learn parameters of the generating network. Further, the method can be used for estimating behaviors, and detecting or tracking objects with high precision, etc.
机译:提供了一种用于通过使用GAN来学习减少翘曲图像中的失真的方法,以增强极端情况下的容错能力和波动鲁棒性。并且该方法包括以下步骤:(a)如果获取了初始图像,则指示生成网络中包括的调整层调整至少一部分初始特征值,从而将初始图像转换为调整后的图像; (b)如果获取了(i)自然度分数,(ii)维护分数和(iii)相似度分数中的至少一部分,则通过参考指示生成网络中包括的损耗层来生成生成网络损耗至少说出自然分数,维护分数和相似度分数,并了解生成网络的参数。此外,该方法可以用于估计行为,以高精度检测或跟踪对象等。

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