Presensitized Plate Defect Classification Based On Fuzzy C-Mean Algorithm

During the production process of the Presensitized Plate, in order to make effective use of the raw material, and to avoid that defective products are mixed into finished products, it’s usually necessary to analyze the raw materials and products. But for a long time, the process of analysis internal mostly depends on manual work to achieve, its results have the characteristics of great subjectivity, too much deviation, etc. This essay is based on the computer image processing technique, and combines the characteristics of Presensitized Plate, and applies Fuzzy C Mean Clustering Algorithm, and then realizes automatic detection of the defects on the Presensitized Plate.We chooses to analyze the texture feature and color characteristics, and defines the fuzzy index size of Fuzzy C Mean Clustering Algorithm in this paper. For the defect information of Presensitized Plate cannot be known in advance,We use a one-dimensional random signal histogram analysis method to determine the class number.Finally. We completed flaw classification based on Fuzzy C Mean Clustering Algorithm theoretically.Do a simulation analysis and verify the effectiveness of the algorithm.