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首页> 外文期刊>International Journal of Advanced Robotic Systems >A coarse-to-fine scheme for groupwise registration of multisensor images
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A coarse-to-fine scheme for groupwise registration of multisensor images

机译：用于多传感器图像的GroupWise登记的粗略方案

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摘要

Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.
机译：集合注册涉及需要同时注册的一组图像。这是挑战，但对于许多图像分析任务，例如车辆检测和医学图像融合很重要。为了有效地解决这个问题，提出了一种用于GroupWise图像配准的新型粗对精细方案。首先，在粗略登记步骤中，未注册的图像被分成参考图像集和浮动图像集。基于分段区域匹配，注册了两组的图像。粗略登记结果用作下一步的初始解决方案。然后，在精细登记步骤中，使用本地模板的高斯混合模型用于建模粗加工图像的关节强度。同时，采用最小消息长度标准的方法来确定未知的混合组件数量。基于该混合模型，最大似然框架用于注册一组图像。为了评估所提出的方法的性能，在不同的图像数据集上比较一些代表性的集团登记方法。实验结果表明，与传统方法相比，该方法具有改善的性能。

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