傅氏变换的自配准性质及其在纹理识别和图象分割中的应用

图象按纹理一致性进行辨识和分割是图象分析中的一个重要问题 ,有着广泛的实际应用 .讨论了傅氏变换应用于纹理识别的机理 ,并基于此提出了一种图象分割算法 .图象中的纹理线条呈现出很多方向 ,并随机地分布在图象的各个位置 ,然而对于它的傅氏变换幅度谱来说 ,相同方向的线条无论其位置如何 ,它们的贡献会被叠加在一起 ,集中地反映在通过频谱中心垂直于原线条方向的条带上 .这一现象被称为傅氏变换幅度谱的自配准性质 .首先对这一性质进行实验个例的研究和理论分析 ,然后设计算法将其应用于图象的纹理辨识和基于纹理的图象分割实验 ,取得了较为满意的效果 .实验证明 ,得益于自配准性质 ,傅氏变换方法不失为一种有潜力的纹理分析和图象分割方法 ,值得进一步扩展更多的图象应用领域

Auto-registration of Fourier Transform Magnitude Spectra and its Application on Texture Identification and Segmentation

Texture identification and segmentation of images are very important issues in image analysis. In this paper we discuss the mechanism of using Fourier transform on texture identification and then suggest a texture image segmentation algorithm based on Fourier transform. Even though the edges of texture images may have various orientations and their locations in the image may be random, for the magnitude of Fourier transform of the image, the contribution of all edges with the same orientation will be stacked up in the orientation being perpendicular to the edges. This special phenomenon is called as auto registration of the magnitude spectra. The auto registration also means a re distribution of contributions of all patterns of the texture, according to the orientations of edges and the frequency locations of patterns rather than spatial locations of them. In this paper we illustrate and theoretically analyse the auto registration property of the magnitude spectra and then propose a method to exploit this property on texture identification and image segmentation. Experimental results show several advantages of this method. It is demonstrated that Fourier transform based method is capable and promising on texture identification and segmentation and a deeper research on this subject is worthwhile doing.