基于多尺度图像处理的图像分析的论文-Go服务器开发

以下是一篇基于多尺度图像处理的图像分析的论文：

题目：基于小波变换和多尺度分析的纹理特征提取算法

摘要：纹理是图像中常见的视觉特征之一，对于图像分类、检索等应用具有重要意义。本文提出了一种基于小波变换和多尺度分析的纹理特征提取算法。首先，采用小波变换对原始图像进行多层分解，得到不同尺度下的子带系数；然后，在每个子带系数上计算局部二阶矩，并将其作为纹理特征表示；最后，通过选择不同尺度下的子带系数和计算方法，获得更加丰富和鲁棒的纹理特征。实验结果表明，该算法能够在不同数据集上达到较高的分类准确率，并且对于光照、旋转等干扰具有一定的鲁棒性。

关键词：小波变换；多尺度分析；纹理特征；图像分类

Abstract: Texture is one of the common visual features in images, which is important for image classification, retrieval and other applications. In this paper, a texture feature extraction algorithm based on wavelet transform and multi-scale analysis is proposed. Firstly, the original image is decomposed into different subband coefficients at different scales by wavelet transform. Then, the local second-order moments are calculated on each subband coefficient as texture feature representation. Finally, more rich and robust texture features are obtained by selecting different subband coefficients and calculation methods at different scales. Experimental results show that the proposed algorithm can achieve high classification accuracy on different datasets, and has certain robustness to interference such as illumination and rotation.

Keywords: Wavelet transform; Multi-scale analysis; Texture feature; Image classification