Initialization of Optimization Methods in Parameter Tuning for Computer Vision Algorithms

Abstract

Computer Vision Algorithms (CVA) are widely used in several applications ranging from security to industrial processes monitoring. In recent years, an interesting emerging application of CVAs is related to the automatic defect detection in some production processes for which quality control is typically performed manually, thus increasing speed and reducing the risk for the operators. The main drawback of using CVAs is represented by their dependence on numerous parameters, making the tuning to obtain the best performance of the CVAs a difficult and extremely time-consuming activity. In addition, the performance evaluation of a specific parameter setting is obtained through the application of the CVA to a test set of images thus requiring a long computing time. Therefore, the problem falls into the category of expensive Black-Box functions optimization. We describe a simple approximate optimization approach to define the best parameter setting for a CVA used to determine defects in a real-life industrial process. The algorithm computationally proved to obtain good selections of parameters in relatively short computing times when compared to the manually determined parameter values.