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

Abstract

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 effectiveness and usability of our approach, we conduct a case study with engineers and obtain feedback from expert interviews.

摘要

机器视觉测量(machine vision measurement, MVM)是一种用于产品质量控制的重要方法, 可有效、无损地测量目标面积或长度。MVM的结果取决于其配置, 尤其是图像采集的打光方案设计和图像处理的算法参数优化。在传统工作流中, 工程师不断调整和验证配置以获得可接受结果, 这非常耗时且严重依赖专业知识。为解决这些挑战, 提出一种目标无关方法, 可视交互式图像聚类, 该方法通过图像聚类推荐具有共同算法参数的打光方案促进配置优化。该方法有4个步骤: 数据准备、数据采样、数据处理和可视分析。在准备阶段, 工程师设计几种候选打光方案获取图像, 并开发算法处理图像。对每张图像, 该方法按照工程师定义的参数采样, 并执行算法获得结果。数据处理的核心是使用算法参数对图像之间关系进行可解释度量。基于图像关系, 开发了一个视觉分析系统, VMExplorer, 帮助工程师图像聚类并探索参数。最后, 工程师可确定合适的打光方案和鲁棒的参数组合。为证明该方法有效性和可用性, 我们与工程师进行案例研究, 并从专家访谈中获得反馈。