图像统计建模在大米加工质量监测中的应用

Application of Image Statistical Model in the Rice Processing-Quality

针对视觉监控系统在感知由大量同质颗粒随机堆积而成的无明显前、背景区分的大米图像中存在的问题,将序贯分裂论引入到图像空间结构视觉分析中。提出一种多尺度全方向高斯导数滤波方法,通过建立图像的韦伯分布模型有效获取大米图像在不同观测尺度下全方向视觉感知特征参量;基于最小二乘-支持向量机原理实现大米加工品质的自动分类识别。大量的验证性和对比性实验证明了所提方法的有效性和优越性。

Vision monitor system is far from effective in the perception of the grainy images comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. To overcome this problem, the sequential fragmentation theory is introduced into the visual analysis of the spatial structure of images. A kind of multi-scale and omni-directional Gaussian derivative filter is presented. The omni-directional structural features of the rice images under different observation scales are achieved by established the image Weibull distribution model. Auto-classification of the rice processing-quality is realized based on the principle of least squares-support vector machine. Abundant confirmative and comparative tests indicate the effectiveness and out- performance of the proposed method.