基于Haar-PP混合特征提取的沉降颗粒识别研究

Research on Settlement Particle Recognition Based on Haar-PP Mixed Feature Extraction

针对沉降颗粒识别与轨迹追踪中由于灰度模糊、变化造成的识别通过率低、准确度差的问题,对由沉降颗粒的个体形态、运动形态以及空间运动引起的动态灰度变化进行分析,完成激光散射像点图像灰度增强、像素特征转换、像素点连通性判断等处理过程,在此基础上提出一种基于Haar-PP混合特征的沉降颗粒识别算法。实验结果表明,与高斯混合模型相比,该算法有较高的识别率,且时间复杂度较低。

To solve the problem that gray blurring and variation cause low recognition rate and poor accuracy for trajectory tracking and trajectory tracking,the processing of gray-scale enhancement,pixel feature conversion and pixel point connectivity of laser scattering image is completed by the analysis of the dynamic gradation changes caused by the individual shape,motion shape and spatial motion of the settled particles.A settlement particle recognition strategy based on Haar-PP mixed feature is proposed.Experimental results show that compared with the Gaussian mixed model,the algorithm has higher recognition rate and lower time complexity.