纹理分析在图象分析和模式识别中起着非常重要的作用，而纹理描述又是其中一个基础的研究课题。由于均匀纹理其局部的灰度分布与空间关系在整体上具有周期性，因此可以用这种局部信息来很好地刻划不同纹理的差异。为了获得良好的纹理分类效果，利用象素点八近邻灰度之间的空间相关性，提出了用八近邻的傅立叶级数作为纹理刻划的新方法。该方法视象素点的八近邻为周期列，并首先通过计算其傅立叶级数，进而来得到整幅图象的局部傅立叶级数图，然后将通过对该傅立叶级数图幅值进行量化而获得的直方图作为纹理描述子（该纹理描述子具有平移旋转不变性）；最后用13种Brodatz纹理来进行分类。实验结果表明，该描述子具有良好的分类能力。

Texture analysis plays an important role in image analysis and pattern recognition, and texture description is a basic research topic of texture analysis. Though texture has not a uniform definition, it is acknowledged that neighborhood properties represent the dominant of a texture. Otherwise the distribution and spatial dependencies of local gray tones are periodic to homogeneous texture. So this local information can be used to describe a texture primitive, and it will work well for texture discrimination. A new texture descriptor is proposed based on the 8-neighbour gray tone spatial dependencies by using 8-neighbour Fourier series. The 8-neighbour of the point are treated as a periodic series, and their Fourier series are computed. Then the local Fourier coefficients map is generated from these local Fourier series of the whole image. A histogram of local Fourier series of the texture image was extracted by quantizing these Fourier coefficients as a texture descriptor. Because the quantization uses only the magnitudes of Fourier coefficients, this descriptor is shift-invariant and rotation-invariant. Experiments on 13 samples of Brodatz textures demonstrate the efficiency and effect of texture classification.