The Identification and Evaluation Model for Test Paper’s Color and Substance Concentration
| Autor: | Jiequan Ou, Jinlan Guan, Minna Chen, Guanghua Liu, Yuting Lai |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Pattern recognition 02 engineering and technology 01 natural sciences Test (assessment) 010309 optics Identification (information) Artificial Intelligence 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 34:2055004 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001420550046 |
| Popis: | The colorimetric method is usually used to test the concentration of substances. However, this method has a big error since different people have different sensitivities to colors. In this paper, in order to solve the identification problem of the color and the concentration of the test paper, firstly, we found out that the concentration of substance is correlated with the color reading by using the Pearson’s Chi-squared test method. And by the concentration coefficient of Pearson correlation analysis, the concentration of substance and color reading is highly correlated. Secondly, according to the RGB value of the paper image, the color moments of the image are calculated as the characteristics of the image, and the Levenberg–Marquardt (LM) neural network is established to classify the concentration of the substance. The accuracy of the training set model is 94.5%, and the accuracy of the test set model is 87.5%. The model precision is high, and the model has stronger generalization ability. Therefore, according to the RGB value of the test paper image, it is effective to establish the LM neural network model to identify the substance concentration. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|