Measurement of Material Surface Defect Intensity by Distributed Cumulative Histogram and Clustering

Abstract

The object of research is a distributed cumulative histogram of a digital image and its advantages for auto-mated determination of the location and intensity of defects of different nature on the surfaces of materials: metal, paper, etc. The technique considered in the study is aimed at minimization of human interference in the process of material surface control from the moment of its photographing to the moment of making a decision about the surface quality.

Three-dimensional distributed cumulative histogram (DCH) is presented as a two-dimensional image in which the pixel intensity corresponds to the third dimension – the number of pixels of a certain intensity in the original surface image. Informative distributed cumulative histogram (IDCH) is used to recognize black, dark and light defects, and to measure their intensity and location by the clustering algorithm. The average value of the pixel intensity in the columns and rows of the pixel matrix of the cumulative histogram image is calculated to estimate the intensity of the defects. Measurement of the intensity of defects is carried out in two ways: directly on the image of the surface sample and by comparing the image of the sample and the reference image of the sample without defects. To solve the problem, an algorithm of hierarchical clustering of data to rectangular segments of the surface image is used. In the image, each cluster is marked with a corresponding color of gray. The image for analysis is transformed using segmentation and inversion algorithms. This allows to get more accurate estimates of the intensity of light and dark defects. The clustering algorithm groups the image segments of the surface samples, as well as the images of the distributed cumulative histogram to group the level of surface damage. Distributed cumulative histogram was used to detect defects on the surface of materials as a method of linking the number and intensity of pixels to image coordinates. Cluster analysis helps to find their coordinates and intensity.

In comparison with known approaches, the method has a linear algorithmic complexity to the number of pixels in the input image, which allows to do a significant number of experiments to identify the types of surfaces of materials for use and the features of algorithms.