Visual interactive image clustering:a target-independent approach for configuration optimization in machine vision measurement

Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.

A Method for Determining Surface Free Energy of Bamboo Fiber Materials by Applying Fowkes Theory and Using Computer Aided Machine Vision Based Measurement Technique

The purpose of this study is to develop a standard methodology for measuring the surface free energy (SFE),and its component parts of bamboo fiber materials.The current methods was reviewed to determine the surface tension of natural fibers and the disadvantages of techniques used were discussed.Although numerous techniques have been employed to characterize surface tension of natural fibers,it seems that the credibility of results obtained may often be dubious.In this paper,critical surface tension estimates were obtained from computer aided machine vision based measurement.Data were then analyzed by the least squares method to estimate the components of SFE.SFE was estimated by least squares analysis and also by Schultz' method.By using the Fowkes method the polar and disperse fractions of the surface free energy of bamboo fiber materials can be obtained.Strictly speaking,this method is based on a combination of the knowledge of Fowkes theory. SFE is desirable when adhesion is required,and it avoids some of the limitations of existing studies which has been proposed.The calculation steps described in this research are only intended to explain the methods.The results show that the method that only determines SFE as a single parameter may be unable to differentiate adequately between bamboo fiber materials,but it is feasible and very efficient.In order to obtain the maximum performance from the computer aided machine vision based measurement instruments,this measurement should be recommended and kept available for reference.