中文摘要：

目的 纹理是描述和区分不同物体的重要特征之一,纹理特征提取一直是模式识别、机器视觉领域的研究热点.局部方向模式（LDP）是一种分辨性好、对随机噪声和非均匀光照鲁棒的纹理特征.而LDP特征由于计算8方向的边缘响应并排序,提取速度较慢.为此对LDP编码方案进行改进.方法 设计了两种改进方案：第1种方案直接对8方向的边缘响应符号进行编码,避开排序,称为FLDP（fast local directional pattern）特征;第2种方案,尝试使用较少的方向模板来降低特征提取的时间、空间消耗,设计了MLDP算子（mini local directional pattern）.结果 在Brodatz数据集的24类均匀纹理图像以及1 1 1类全部纹理图像上将本文提出的FLDP特征、MLDP特征与传统的LDP进行了对比实验.实验结果表明,在保证了分类准确率的前提下,FLDP算子的运算速度是3th-LDP的20倍左右,MLDP算子的运算速度是3th-LDP的35倍左右.结论 论文设计了2种方案改进了LDP特征,分别为FLDP算子和MLDP算子.实验结果表明,这两种改进方案,在保证分类准确率的同时,大幅度提高了特征提取运算速度.

英文摘要：

Objective Texture is an important feature for describing different kinds of materials. Texture feature extraction is a hot topic in pattern recognition and computer vision research fields. LDP descriptor is a discriminative texture feature which is more robust to random noise and non-monotonic illumination variation than LBP. However, the LDP descriptor is much slow for calculating and sorting edge responses of 8 directions. To solve this problem, we improve the LDP coding method. Method We propose two improved methods. The first one adapts the same edge templates as LDP but uses a differ- ent coding scheme without sorting, which we called FLDP （fast local directional pattern） . The second method uses less edge templates to construct short descriptor in order to reduce time and storage consumption of the feature, which we called MLDP （mini local directional pattern） . Result We present experimental results on the Brodatz full set and Brodatz sub- set. Both show that the FLDP descriptor is 19 times faster than the LDP and the MLDP descriptors are 34 times faster with even better performance. Conclusion Two methods are presented in this paper, FLDP and MLDP, to improve the LDP. Experiments show that these two improved descriptors cost much less time with even better performance than the LDP.