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Feature extraction is important in image matching. However, the perspective deformations, especially the anisotropic scaling deformations will affect the performances of feature extraction algorithms. To improve the image matching results when notable perspective deformations exist, an algorithm for extracting feature points and covariant regions is introduced in this paper. We propose using a new type of feature points, the &#x201C;inside corner points&#x201D; as seed points. And we propose using a multi-scale seeded region growing method to find the local support regions for feature points. Based on the shapes of local support regions, an image patch around a feature point can be rectified by doing shape normalization, and the anisotropic scaling deformations can be reduced by the rectification. By doing image matching with these rectified image patches, the matching results are notably improved.

Non-iterative Covariant Feature Extraction Based on the Shapes of Local Support Regions