Study On Granularity Measurement Method Using Image Processing

Granularity is an important quality index of granular material. It exerts significant influence on technical performance of products and plays key role in granule application. At present the routine methods, for example, sedimentation and fractionation technique are chiefly adopted in measurement of granularity. There are some disadvantages in these routine methods, for example, they take many steps and cast much time, and the accuracy of results is affected greatly by subjective factors of human inspectors. The measurement technology of granularity using image processing can overcome shortage of routine methods, and so it is one of the most effective methods for measurement of granularity.In this paper, the contrast of original image is enhanced by nonlinear transformation based on the characteristics of granule image; After comparing several methods of threshold segmentation and analyzing their performance and applicability on granule image, the adaptive threshold segmentation based on method of maximum classes square error is presented; The noise of binary image is reduced with morphological filter, the hollow in binary image is filled in and a recognition algorithm is designed to remove agglomerated granule; on the basis of Watershed Method are adopted to segment agglomerate granules that are not agglomerated tightly; The parameters of size and shape, perimeter, area, diameter, shape index, longest Feret diameter, shortest Feret diameter etc, are extracted. This technique is applied in measurement of granularity.This subject adopts object-oriented develop method under the VC++6.0 environment. The system has characteristics of high-efficient, accurate and easy to use because it realizes the functions of automatic segmentation, cutting apart, edge following, measuring, storing of granule etc, can have been heavy to raise the speed and accuracy of granule measurements reduce the artificial error.Experiments proved that method of image processing techniques of granularity could determine granule distribution clearly and directly. The result of inspection is speedy and stable.