基于切比雪夫不等式的紫色土彩色图像分割

野外采集的机器视觉图像往往包含复杂背景,会对机器视觉识别紫色土产生影响,为了避免背景干扰,分割提取紫色土区域图像是首要的工作.本文应用3×3小子阵的标准差测度,建立模型优化紫色土区域的土壤与杂质类间和类内方差比,获得优化的置信概率P和H域分割阈值,提出了一种基于切比雪夫不等式的自适应H阈值分割算法,实现基于图像自身紫色土特征的自适应分割,提升初分割出紫色土区域图像的精度.针对初分割结果中的孤立点、离散小土块和空洞,提出了从图像中心点出发的剔除背景区域孤立点和离散小土块的螺旋生长算法和基于4方向边界点确认的紫色土区域的空洞填充算法.仿真实验结果显示:自适应切比雪夫阈值分割算法与螺旋生长算法和空洞填充算法结合,分割提取出紫色土区域图像的误分率降低到3.24%,总时间花销更少,算法是有效的.

Color Image Segmentation of Purple Soil Based on Chebyshev Inequality

The vision images of the types of purple soils picked up in the field with an identification system based on machine vision inevitably contain the complex background. To avoid background interference, segmentation extraction of the images of the purple soil area is essential. In this paper, an optimization model is established by using standard deviation measure of the 3×3 small subarray, which minimizes the ratio of inter-class variance to intra-class variance between soil and impurities in the purple soil area. The optimized confidence probability P and the segment threshold of H domain are obtained, and an adaptive segmentation algorithm based on the Chebyshev inequality is developed by the optimization model. The algorithm realizes the adaptive segmentation based on the purple soil features in the image and improves the accuracy of preliminary segmenting of the image of purple soil area. Then, a spiral growth algorithm and a hole-filling algorithm are proposed to process the raw segmentation result. They remove the isolated points and small soil block from the background area and fill in the hole in the purple soil area to extract the image of the purple soil area from the vision image. The simulation results show that the error rate of the proposed method is reduced to 3.24% and the total time spent is less. The proposed adaptive Chebyshev threshold segmentation algorithm combined with spiral growth algorithm and cavity filling algorithm can effectively extract purple soil region image.