Adaptive Feature Point Image Registration Algorithm with Added Spatial Constraint Model

Q3 Decision Sciences
Xiao Zhou;Songlin Yu;Jijun Wang;Yuhua Chen;Fangyuan Li;Yan Li
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引用次数: 0

Abstract

Image data with different spectral features contain different attribute information of a target, which is naturally complementary and can provide more comprehensive and detailed features after registration and fusion. Image registration methods based on point features have the advantages of high speed and precision, and have been widely used in visible light image registration. For registration of multiscale images and those with different spectral characteristics, the precision of these methods is affected by such factors as complex gradient variation. To this end, we add a spatial constraint model to point feature image registration, and improve the method from the aspects of feature point selection, registration, and image conversion parameter calculation. The method is applied to different types of image registration programs, and the results show that it can effectively improve the registration accuracy of multiscale images with different spectral characteristics.
添加空间约束模型的自适应特征点图像配准算法
具有不同光谱特征的图像数据包含目标的不同属性信息,这是自然互补的,经过配准和融合后可以提供更全面、更详细的特征。基于点特征的图像配准方法具有速度快、精度高的优点,在可见光图像配准中得到了广泛的应用。对于多尺度图像和具有不同光谱特征的图像的配准,这些方法的精度受到复杂梯度变化等因素的影响。为此,我们在点特征图像配准中加入了空间约束模型,并从特征点选择、配准和图像转换参数计算等方面对该方法进行了改进。将该方法应用于不同类型的图像配准程序,结果表明,该方法可以有效地提高不同光谱特征的多尺度图像的配准精度。
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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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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