Abstract

Texture analysis is a significant area in image processing, but feature point extraction is pretty susceptible to noise in texture images. In this paper, a new method for extracting feature points, especially from the paper overlapping images, is presented, which is using dynamic threshold, mathematical morphology and image thinning to extract potential feature points. And an optimization algorithm is also proposed to promote the repeatability of feature points via analyzing corresponding skeletons. Results show that the proposed algorithm could depress noise, and the repeatability of this method (60%) outperforms traditional feature extraction algorithms, like Harris (46%), FAST (57%), and SUSAN (45%), in paper overlapping images.

中文翻译：

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一种改进的基于纹理图像骨架的角点检测器

摘要

纹理分析是图像处理中的一个重要领域，但特征点提取对纹理图像中的噪声非常敏感。本文提出了一种提取特征点的新方法，特别是从重叠图像中提取特征点，即利用动态阈值、数学形态学和图像细化来提取潜在特征点。并提出了一种优化算法，通过分析相应的骨架来提高特征点的可重复性。结果表明，该算法可以抑制噪声，并且该方法的重复性（60%）优于传统特征提取算法，如Harris（46%）、FAST（57%）和SUSAN（45%），在纸张重叠图像中.