使用各向异性扩散改进LBP及其变体
Improving LBP and its variants using anisotropic diffusion
摘要：
本文的主要目的是提出一种新的预处理步骤，以改善局部特征描述符和纹理分类。预处理是通过使用转换，这有助于突出的显着特征，在纹理识别中发挥重要作用。我们评估和比较四种不同的竞争方法：三种不同的各向异性扩散方法，包括经典的各向异性Perona-Malik扩散和随后的两个正则化它和高斯核的应用，这是纹理分析中的经典多尺度方法。分析了变换后的图像与原始图像的结合情况。结果表明，使用的预处理步骤确实导致改进的纹理识别。
英文摘要：
The main purpose of this paper is to propose a new preprocessing step in order to improve local feature descriptors and texture classification. Preprocessing is implemented by using transformations which help highlight salient features that play a significant role in texture recognition. We evaluate and compare four different competing methods: three different anisotropic diffusion methods including the classical anisotropic Perona-Malik diffusion and two subsequent regularizations of it and the application of a Gaussian kernel, which is the classical multiscale approach in texture analysis. The combination of the transformed images and the original ones are analyzed. The results show that the use of the preprocessing step does lead to improved texture recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
