Evaluation of image texture recognition techniques in application to wastewater coagulation

Flocs formation and growth are important characteristics in wastewater coagulation process. The shape and size of flocs highly affect further separation processes, therefore resulting treatment efficiency of wastewater after coagulation. Observed images of flocs tend to show strong relations to coagulation parameters: dose and coagulation time. In this article, three texture recognition techniques were evaluated for the ability to mathematically describe the relationship between the images of flocs and coagulant dosages. The easily computable texture analysis methods were found to be potential techniques for the characterization of the particles images. Ten out of eleven co-occurrence matrix-based grey level co-occurrence matrix (GLCM) texture features were found to be significant for the dosage prediction by a principal component regression model with only one principal component. Two features (Inverse difference moment and Variance) were selected for the multiple linear regression model. Test set prediction accuracy varied from 83 to 96% depending on texture analysis method and multivariate model. Best dosage prediction and image classification results were achieved by GLCM and angle measure technique. The results of image texture analysis coupled with multivariate modelling techniques indicate that it is possible to characterize and relate flocs images, captured during coagulation, with different coagulant dosages, as well as predict those dosages.