图像配准的抽样分析
Finite-Sample Analysis of Image Registration
摘要：
我们研究的问题，在有限分辨率制度的图像配准和表征的错误概率的算法作为一个功能的属性的变换和图像捕获噪声。具体来说，我们定义了一个信道感知的范斯坦解码器，以获得有限分辨率下的最小可实现的错误概率的上界。我们特别关注高阶项，并使用Berry-Esseen型CLTs来获得问题的可扩展性条件的更强表征。然后，我们推导出一个强大的类型计数结果来表征MMI解码器的性能方面的最大似然解码器，在一个简化的设置的问题。然后，我们描述了如何分析，当涉及到从通道感知上下文的结果提供更强的表征的有限样本的通用图像配准的性能。
英文摘要：
We study the problem of image registration in the finite-resolution regime and characterize the error probability of algorithms as a function of properties of the transformation and the image capture noise. Specifically, we define a channel-aware Feinstein decoder to obtain upper bounds on the minimum achievable error probability under finite resolution. We specifically focus on the higher-order terms and use Berry-Esseen type CLTs to obtain a stronger characterization of the achievability condition for the problem. Then, we derive a strong type-counting result to characterize the performance of the MMI decoder in terms of the maximum likelihood decoder, in a simplified setting of the problem. We then describe how this analysis, when related to the results from the channel-aware context provide stronger characterization of the finite-sample performance of universal image registration.
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