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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Affine Invariant Feature Extraction based on the Shape of Local Support Region
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Affine Invariant Feature Extraction based on the Shape of Local Support Region

机译：基于局部支持区域形状的仿射不变特征提取

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Feature extraction is an important step in image feature matching. And the repeatability of features is particularly crucial. The perspective deformation of images can decrease the repeatability of features. This paper introduces a feature extraction method which can improve the repeatability of features when notable perspective deformation exists. First, initial feature points are extracted by the classical Harris algorithm. Then a local support region is extracted for every initial feature point. Affine rectification parameters can be calculated based on the shape of the support region. Then the image patch around a feature point is resampled using these affine rectification parameters. The final feature points are extracted and described on the resampled image patches. The repeatability of the final features is much better than initial features thanks to the affine rectification. And the feature descriptors obtained on the resampled image patches are better to be used in image matching.
机译：特征提取是图像特征匹配的重要步骤。功能的可重复性尤其重要。图像的透视变形会降低特征的可重复性。本文介绍一种特征提取方法，当存在明显的透视变形时，该方法可以提高特征的可重复性。首先，通过经典的哈里斯算法提取初始特征点。然后，为每个初始特征点提取局部支持区域。可以基于支撑区域的形状来计算仿射整流参数。然后，使用这些仿射校正参数对特征点周围的图像块进行重新采样。提取最终特征点并在重新采样的图像块上进行描述。由于仿射校正，最终特征的可重复性比初始特征好得多。在重采样的图像块上获得的特征描述符最好用于图像匹配。

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