边缘信息往往是视觉对象检测的关键，已有方法对边缘梯度在各个方向上进行计算，会导致计算冗余.受韦伯局部描述算子的启发，提出一种模拟人眼观察事物的发散性及显著性特点的纹理结构算子.首先，计算邻域像素与中心像素灰度值相对差的总和，除以中心像素的灰度值求出局部显著性因子；然后，通过中心发散灰度共生矩阵提取局部纹理结构；最后，构造二维直方图结合显著性因子和纹理结构，生成一定维数的显著性纹理结构特征描述算子.实验结果表明，该特征算子具有良好的边缘检测能力，应对噪声和明暗变化的鲁棒性以及强大的结构表达能力，其行人检测的准确率优于中心变换直方图和梯度方向直方图.对车辆主动安全有很高的应用价值.

Edge information is often the key to the detection of objects. Traditional edge detection algorithms calculate gradient omnidirectionally, which usually results in calculation of redundancy. Inspired by Weber local descriptor, this study proposes a saliency texture structure descriptor that simulates divergent and significant characteristics of human eyes observing things. First of all, it calculates the sum of relative differences between intensity of a center pixel and those of its neighborhood pixels, and divide the sum by the center pixel's intensity to get its local saliency factor. Then, it extracts its local texture structure though a divergent gray level co-occurrence matrix. At last, it constructs a two dimensional histogram as the feature vectors by combining saliency factor and texture structure. Experimental results show that the saliency texture structure descriptor has the ability of good edge detection and powerful structural expression, and is robust to noise and light and shadow changes. When used in pedestrian detection, the saliency texture structure descriptor gets much higher detection rate than other local descriptors such as CENTRIST and HOG. This descriptor can find its high application value in vehicle active safety system.

国家自然科学基金（61272062）