The presented work reports on the progress of our methodology and framework for automated image processing and analysis systém design for industrial vision application. We focus on the important task of automated texture analysis, which is an essential component of automated quality-control systems. In this context, the portfolio of texture operators and assessment methods has been enlarged. Optimized operator parameterization is investigated using particle swarm optimization (PSO). A particular goal of this work is the investigation of support vector machines (SVM) as alternative assessment method for the operator parameter optimization, incorporating the efficient inclusion of SVM parameter settings in this optimization. Methods of the enhanced portfolio were applied employing benchmark textures, real application data from leather inspection, and synthetic textures including defects, specially designed to industrial needs. The key results of our work are that SVM is a highly esteemed and powerful assessment and classification method and parameter optimization, based, e.g., on SVM/PSO of standard and proprietary texture operators boosted performance in all cases. However, the appropriateness of a certain operator proved to be highly data-dependent, which advocates our methodology even more. Thus, the operator selection has been included and investigated for the synthetic textures. Summarising, our work provides a generic texture analysis system, even for unskilled users, that is automatically configured to the application. The method portfolio will be enlarged in future work.
This paper presents a hybrid method to predict tunnel surrounding rock displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding rock displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding rock displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding rock displacement prediction.