Multimodal image registration technique based on improved local feature descriptors
Multimodal image registration technique based on improved local feature descriptors
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DOI：
10.1117/1.jei.24.1.013013
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发表时间：
2015
影响因子：
1.1
通讯作者：
S. Teng;Md. Tanvir Hossain;Guojun Lu
中科院分区：
文献类型：
--
作者：
S. Teng;Md. Tanvir Hossain;Guojun Lu
抽象的。在过去的十年中，多模式图像配准受到了极大的关注，而且大多数技术都是全球性的。虽然局部配准技术被广泛应用于一般的图像配准，但对于多模式图像配准的研究却非常有限。尺度不变特征变换(SIFT)是一种广为人知的通用图像配准技术。然而，SIFT描述符对于多模式并不是不变的。我们提出了一种基于SIFT的方法，该方法既具有形态不变性，又保持了局部方法的优点。此外，我们提出的直方图权重策略也提高了描述符匹配的精度，这是图像配准的重要步骤。因此，我们提出的策略不仅可以提高多模式配准的精度，而且有可能提高所有基于SIFT的应用程序的性能，例如一般的图像配准和目标识别。
Abstract. Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.