This note describes a method for detecting dense random texture using fully connected points sampled on image edges. An edge image is randomly sampled with points, the standard L2 distance is calculated between all connected points in a neighbourhood. For each point, a check is made if the point intersects with an image edge. If this is the case, a unity value is added to the distance, otherwise zero. From this an edge excess index is calculated for the fully connected edge graph in the range [1.0..2.0], where 1.0 indicate no edges. The ratio can be interpreted as a sampled Bernoulli process with unknown probability. The Bayesian posterior estimate of the probability can be associated with its conjugate prior which is a Beta($\alpha$, $\beta$) distribution, with hyper parameters $\alpha$ and $\beta$ related to the number of edge crossings. Low values of $\beta$ indicate a texture rich area, higher values less rich. The method has been applied to real-time SLAM-based moving object detection, where points are confined to tracked boxes (rois).

翻译：本文介绍一种利用图像边缘上全连接采样点检测稠密随机纹理的方法。首先在边缘图像上随机采样点，计算邻域内所有连接点之间的标准L2距离。针对每个点，检测其是否与图像边缘相交：若相交则在距离值上加1，否则加0。由此计算全连接边缘图的边缘超额指数，其取值范围为[1.0..2.0]，其中1.0表示无边缘。该比率可视为未知概率的伯努利采样过程。概率的贝叶斯后验估计与其共轭先验相关联，该先验为贝塔分布Beta($\alpha$, $\beta$)，其中超参数$\alpha$和$\beta$与边缘穿越次数相关。$\beta$值越小表示纹理丰富区域，值越大则纹理丰富度越低。该方法已应用于基于实时SLAM的运动目标检测，其中采样点被限定在跟踪框（rois）内。