提出了一种新的基于局部纹理分析的虹膜识别算法。
In this paper, we proposed a new iris recognition method based on the analysis of local regions.
详细说明：图像纹理分析的好的源代码，可以计算纹理的二阶特征：能量，熵，局部稳定性，惯性距，和相关性。
Image texture analysis of good source code can calculate the second-order texture characteristics: energy, entropy, local stability, inertial distance, and relevance.
该算法以具有多分辨率特性的小波包为基础，结合结构法的纹理元分析方式，采用区域质心完成局部特征提取，最后使用投影法进行整体的特征提取。
The texture feature was obtained by statistically projecting the local centroid which was referred from the structural texture analysis methods based on the wavelet package transform.
介绍了一种利用局部沃尔什变换(LWT)提取图像纹理特征的新方法，给出lwt的定义，并分析了LWT系数的统计特性及其各阶矩的纹理鉴别性能。
A new texture feature extraction method using Local Walsh Transform (LWT) is presented. The definition of LWT is given. The statistical properties of LWT coefficients are analyzed.
局部差分变换具有灰度线性不变性，可消除光照变化对纹理分析的影响。
Local difference transform is invariant against any monotonic transformation of the gray scale.
通过分析阴影的物理模型，得出局部纹理的光照不变性，利用基于比值判决的LBP纹理法来区分运动车辆和阴影，并应用亮度约束和几何启发式准则进一步改善阴影检测效果。
The distribution of improved LBP texture is discussed and a significant test is performed to classify each moving pixel into foreground object or moving shadow according to this theory.
通过分析阴影的物理模型，得出局部纹理的光照不变性，利用基于比值判决的LBP纹理法来区分运动车辆和阴影，并应用亮度约束和几何启发式准则进一步改善阴影检测效果。
The distribution of improved LBP texture is discussed and a significant test is performed to classify each moving pixel into foreground object or moving shadow according to this theory.
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