Detection of Impurity within Grain Samples by Image Analysis

Abstract

The content of impurities in a batch of grain is an important index for grain storage and grain quality standard evaluation. In order to improve the measurement reliability and real-time capability, one new impurity separating and counting system is presented, which integrates the image enhancement, image segmentation and morphological image processing algorithm for impurity separation in doped grain. Firstly, histogram equalization and Gauss-Laplacian operator are used to enhance the gray difference between grains and impurities; then the parameters of expansion and area of impurities are introduced to remove false points, and each impurity edge is extracted by Roberts operator; finally, all the impurities are labeled and counted. Experimental analysis shows the effectiveness of the proposed algorithm.