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

作　　者：Lvhan PAN Guodao SUN Baofeng CHANG Wang XIA Qi JIANG Jingwei TANG Ronghua LIANG 

机构地区：[1]College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310012,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2023年第3期355-372,共18页信息与电子工程前沿（英文版）

基　　金：Project supported by the National Key R&D Program of China(No.2020YFB1707700);the Zhejiang Provincial Natural Science Foundation of China(No.LR23F020003);the National Nat-ural Science Foundation of China(Nos.61972356 and 62036009)。

摘　　要：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.

关 键 词：Machine vision measurement Lighting scheme design Parameter optimization Visual interactive image clustering 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]