Boosting cross-modality image registration

Adrian Barbu, R. Ionasec
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引用次数: 7

Abstract

Cross-modality image registration is a difficult problem because the same structures have different intensity patterns in the two modalities, making straightforward methods based on SSD or cross-correlation not applicable. This paper presents a learning based approach to cross-modality image registration. First, it describes a method to map the image registration problem into a problem of binary classification. Then, it presents a method to select a number of image registration algorithms from a larger pool and combine them by AdaBoost into a boosted algorithm that is more accurate than any of the algorithms in the pool. Finally, it presents a method named virtual boosting that allows to directly obtain the result of the boosted algorithm without performing any parameter search. In our cross-modality image registration application, the algorithm pool consists of many feature-based registration algorithms with different configurations. An experimental validation on the registration of thousands of aerial video frames with satellite images from Google Maps showed that the boosted algorithm has a 20–30% smaller error than the best registration algorithm from the pool (based on SIFT features). More generally, the method presented can be applied to combine a number of algorithms aimed at solving the same problem into a boosted algorithm that is more accurate than any of them.
增强跨模态图像配准
由于相同的结构在两种模态中具有不同的强度模式,使得基于SSD或互相关的直接方法不适用,因此跨模态图像配准是一个难题。提出了一种基于学习的跨模态图像配准方法。首先,描述了一种将图像配准问题映射为二值分类问题的方法。然后,提出了一种从较大的图像配准算法池中选择多个图像配准算法的方法,并通过AdaBoost将它们组合成一个比池中任何算法都更精确的增强算法。最后,提出了一种名为虚拟增强的方法,可以直接获得增强算法的结果,而无需进行任何参数搜索。在我们的跨模态图像配准应用中,算法池由许多不同配置的基于特征的配准算法组成。对来自Google Maps的数千个航拍视频帧与卫星图像进行配准的实验验证表明,增强算法比池中的最佳配准算法(基于SIFT特征)误差小20-30%。更一般地说,所提出的方法可以应用于将许多旨在解决相同问题的算法组合成一个比其中任何一个都更准确的增强算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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