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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Data driven image models through continuous joint alignment
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Data driven image models through continuous joint alignment

机译：通过连续的关节对齐以数据驱动的图像模型

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This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance" variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images - i.e., the images without the nuisance variables - we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are shared between classes, one can share the knowledge learned in one task with another task, leading to efficient learning. We demonstrate this process by building a handwritten digit classifier from just a single example of each class. In addition to applications in handwritten character recognition, we describe in detail the application of bias removal from magnetic resonance images. Unlike previous methods, we use a separate, nonparametric model for the intensity values at each pixel. This allows us to leverage the data from the MR images of different patients to remove bias from each other. Only very weak assumptions are made about the distributions of intensity values in the images. In addition to the digit and MR applications, we discuss a number of other uses of congealing and describe experiments about the robustness and consistency of the method.
机译：本文介绍了一种称为凝结的技术，用于根据数据对图像类进行建模。这个想法是从一组图像开始，并通过消除沿已知变化轴的变化来使其看起来尽可能相似。此技术可用于消除“麻烦的”变量，例如手写数字的仿射变形或磁共振图像中不需要的偏置场。除了分离和建模潜像（即没有麻烦变量的图像）之外，我们还可以对麻烦变量本身进行建模，从而生成分解式生成图像模型。当班级之间共享烦人的变量分布时，一个人可以与另一个任务共享在一个任务中学习的知识，从而提高学习效率。我们通过仅从每个类的一个示例构建手写数字分类器来演示此过程。除了在手写字符识别中的应用外，我们还将详细介绍从磁共振图像中消除偏倚的应用。与以前的方法不同，我们对每个像素的强度值使用单独的非参数模型。这使我们能够利用来自不同患者的MR图像的数据来消除彼此的偏差。关于图像中强度值的分布仅做出非常弱的假设。除了数字和MR应用之外，我们还讨论了凝结的其他许多用途，并描述了有关该方法的鲁棒性和一致性的实验。

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