可视交互式图像聚类：一种机器视觉测量中目标无关的配置优化方法

潘律翰; 孙国道; 常宝峰; 夏旺; 江棨; 汤井威; 梁荣华

doi:10.1631/FITEE.2200547

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可视交互式图像聚类：一种机器视觉测量中目标无关的配置优化方法

常规文章 | Updated：2023-03-24

- 可视交互式图像聚类：一种机器视觉测量中目标无关的配置优化方法
- Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement
- 信息与电子工程前沿（英文） 2023年24卷第3期页码：355-372
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310012, China
- Author bio：
  
  †E-mail: lvhanpan@zjut.edu.cn
  ‡ Corresponding author
- Funds：
  
  National Key R&D Program of China(2020YFB1707700);Zhejiang Provincial Natural Science Foundation of China(LR23F020003);National Natural Science Foundation of China(61972356;62036009)
- DOI：10.1631/FITEE.2200547
  中图分类号： TP39
- 收稿：2022-11-05，
  
  录用：2023-01-05，
  
  纸质出版：2023-03-0
- Accepted：
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潘律翰, 孙国道, 常宝峰, 等. 可视交互式图像聚类：一种机器视觉测量中目标无关的配置优化方法[J]. 信息与电子工程前沿（英文）, 2023,24(3):355-372.

Lvhan PAN, Guodao SUN, Baofeng CHANG, et al. Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(3): 355-372.
潘律翰, 孙国道, 常宝峰, 等. 可视交互式图像聚类：一种机器视觉测量中目标无关的配置优化方法[J]. 信息与电子工程前沿（英文）, 2023,24(3):355-372. DOI： 10.1631/FITEE.2200547.

Lvhan PAN, Guodao SUN, Baofeng CHANG, et al. Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(3): 355-372. DOI： 10.1631/FITEE.2200547.

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。

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.

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references

Afzal S , Maciejewski R , Ebert DS , 2011 . Visual analytics decision support environment for epidemic modeling and response evaluation . IEEE Conf on Visual Analytics Science and Technology , p. 191 - 200 . doi: 10.1109/VAST.2011.6102457 http://doi.org/10.1109/VAST.2011.6102457

Albers A , Gladysz B , Pinner T , et al. , 2016 . Procedure for defining the system of objectives in the initial phase of an Industry 4.0 project focusing on intelligent quality control systems . Proc CIRP , 52 : 262 - 267 . doi: 10.1016/j.procir.2016.07.067 http://doi.org/10.1016/j.procir.2016.07.067

Alonso V , Dacal-Nieto A , Barreto L , et al. , 2019 . Industry 4.0 implications in machine vision metrology: an overview . Proc Manuf , 41 : 359 - 366 . doi: 10.1016/j.promfg.2019.09.020 http://doi.org/10.1016/j.promfg.2019.09.020

Alsallakh B , Ren L , 2017 . PowerSet: a comprehensive visualization of set intersections . IEEE Trans Visual Comput Graph , 23 ( 1 ): 361 - 370 . doi: 10.1109/TVCG.2016.2598496 http://doi.org/10.1109/TVCG.2016.2598496

Alsallakh B , Micallef L , Aigner W , et al. , 2016 . The state-of-the-art of set visualization . Comput Graph Forum , 35 ( 1 ): 234 - 260 . doi: 10.1111/cgf.12722 http://doi.org/10.1111/cgf.12722

Baron ME , 1969 . A note on the historical development of logic diagrams: Leibniz, Euler and Venn . Math Gaz , 53 ( 384 ): 113 - 125 . doi: 10.2307/3614533 http://doi.org/10.2307/3614533

Bergner S , Sedlmair M , Moller T , et al. , 2013 . ParaGlide: interactive parameter space partitioning for computer simulations . IEEE Trans Visual Comput Graph , 19 ( 9 ): 1499 - 1512 . doi: 10.1109/TVCG.2013.61 http://doi.org/10.1109/TVCG.2013.61

Bruckner S , Möller T , 2010 . Result-driven exploration of simulation parameter spaces for visual effects design . IEEE Trans Visual Comput Graph , 16 ( 6 ): 1468 - 1476 . doi: 10.1109/TVCG.2010.190 http://doi.org/10.1109/TVCG.2010.190

Coffey D , Lin CL , Erdman AG , et al. , 2013 . Design by dragging: an interface for creative forward and inverse design with simulation ensembles . IEEE Trans Visual Comput Graph , 19 ( 12 ): 2783 - 2791 . doi: 10.1109/TVCG.2013.147 http://doi.org/10.1109/TVCG.2013.147

Comer ML , III Delp EJ , 1999 . Morphological operations for color image processing . J Electron Imag , 8 ( 3 ): 279 - 289 . doi: 10.1117/1.482677 http://doi.org/10.1117/1.482677

Eades P , 1984 . A heuristic for graph drawing . Congre Numer , 42 : 149 - 160 .

Gleicher M , 2018 . Considerations for visualizing comparison . IEEE Trans Visual Comput Graph , 24 ( 1 ): 413 - 423 . doi: 10.1109/TVCG.2017.2744199 http://doi.org/10.1109/TVCG.2017.2744199

Godina R , Matias JCO , 2019 . Quality control in the context of Industry 4.0 . Int Joint Conf on Industrial Engineering and Operations Management , p. 177 - 187 . doi: 10.1007/978-3-030-14973-4_17 http://doi.org/10.1007/978-3-030-14973-4_17

Golnabi H , Asadpour A , 2007 . Design and application of industrial machine vision systems . Robot Comput-Integr Manuf , 23 ( 6 ): 630 - 637 . doi: 10.1016/j.rcim.2007.02.005 http://doi.org/10.1016/j.rcim.2007.02.005

Kopparapu SK , 2006 . Lighting design for machine vision application . Image Vis Comput , 24 ( 7 ): 720 - 726 . doi: 10.1016/j.imavis.2005.12.016 http://doi.org/10.1016/j.imavis.2005.12.016

Lex A , Gehlenborg N , Strobelt H , et al. , 2014 . UpSet: visualization of intersecting sets . IEEE Trans Visual Comput Graph , 20 ( 12 ): 1983 - 1992 . doi: 10.1109/TVCG.2014.2346248 http://doi.org/10.1109/TVCG.2014.2346248

Luk F , Huynh V , North W , 1989 . Measurement of surface roughness by a machine vision system . J Phys E Sci Instrum , 22 ( 12 ): 977 - 980 . doi: 10.1088/0022-3735/22/12/001 http://doi.org/10.1088/0022-3735/22/12/001

Martin D , 2007 . A Practical Guide to Machine Vision Lighting . Advanced Illumination , Vermont, USA , p. 1 - 21 .

Munzner T , 2014 . Visualization Analysis and Design . CRC Press , New York, USA .

Ngo NV , Hsu QC , Hsiao WL , et al. , 2017 . Development of a simple three-dimensional machine-vision measurement system for in-process mechanical parts . Adv Mech Eng , 9 ( 10 ): 1 - 11 . doi: 10.1177/1687814017717183 http://doi.org/10.1177/1687814017717183

Pretorius AJ , Bray MA , Carpenter AE , et al. , 2011 . Visualization of parameter space for image analysis . IEEE Trans Visual Comput Graph , 17 ( 12 ): 2402 - 2411 . doi: 10.1109/TVCG.2011.253 http://doi.org/10.1109/TVCG.2011.253

Pretorius AJ , Zhou Y , Ruddle R , 2015 . Visual parameter optimisation for biomedical image processing . BMC Bioinform , 16 ( S11 ): S9 . doi: 10.1186/1471-2105-16-S11-S9 http://doi.org/10.1186/1471-2105-16-S11-S9

Sadana R , Major T , Dove A , et al. , 2014 . OnSet: a visualization technique for large-scale binary set data . IEEE Trans Visual Comput Graph , 20 ( 12 ): 1993 - 2002 . doi: 10.1109/TVCG.2014.2346249 http://doi.org/10.1109/TVCG.2014.2346249

Sahoo PK , Soltani S , Wong AKC , 1988 . A survey of thresholding techniques . Comput Vis Graph Image Process , 41 ( 2 ): 233 - 260 . doi: 10.1016/0734-189X(88)90022-9 http://doi.org/10.1016/0734-189X(88)90022-9

Sedlmair M , Heinzl C , Bruckner S , et al. , 2014 . Visual parameter space analysis: a conceptual framework . IEEE Trans Visual Comput Graph , 20 ( 12 ): 2161 - 2170 . doi: 10.1109/TVCG.2014.2346321 http://doi.org/10.1109/TVCG.2014.2346321

Torsney-Weir T , Saad A , Moller T , et al. , 2011 . Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration . IEEE Trans Visual Comput Graph , 17 ( 12 ): 1892 - 1901 . doi: 10.1109/TVCG.2011.248 http://doi.org/10.1109/TVCG.2011.248

Wang WC , Guan FN , Ma SY , et al. , 2015 . Measurement system of gear parameters based on machine vision . Meas Contr , 48 ( 8 ): 242 - 248 . doi: 10.1177/0020294015595997 http://doi.org/10.1177/0020294015595997

Weng L , Preneel B , 2011 . A secure perceptual hash algorithm for image content authentication . IFIP Int Conf on Communications and Multimedia Security , p. 108 - 121 . doi: 10.1007/978-3-642-24712-5_9 http://doi.org/10.1007/978-3-642-24712-5_9

Wu KL , Yang MS , 2007 . Mean shift-based clustering . Patt Recogn , 40 ( 11 ): 3035 - 3052 . doi: 10.1016/j.patcog.2007.02.006 http://doi.org/10.1016/j.patcog.2007.02.006

Xia JZ , Ye FJ , Chen W , et al. , 2018 . LDSScanner: exploratory analysis of low-dimensional structures in high-dimensional datasets . IEEE Trans Visual Comput Graph , 24 ( 1 ): 236 - 245 . doi: 10.1109/TVCG.2017.2744098 http://doi.org/10.1109/TVCG.2017.2744098

Xia JZ , Chen TX , Zhang L , et al. , 2020 . SMAP: a joint dimensionality reduction scheme for secure multi-party visualization . IEEE Conf on Visual Analytics Science and Technology , p. 107 - 118 . doi: 10.1109/VAST50239.2020.00015 http://doi.org/10.1109/VAST50239.2020.00015

Xia JZ , Zhang YC , Song J , et al. , 2022 . Revisiting dimensionality reduction techniques for visual cluster analysis: an empirical study . IEEE Trans Visual Comput Graph , 28 ( 1 ): 529 - 539 . doi: 10.1109/TVCG.2021.3114694 http://doi.org/10.1109/TVCG.2021.3114694

Xia JZ , Huang LQ , Lin WX , et al. , 2023 . Interactive visual cluster analysis by contrastive dimensionality reduction . IEEE Trans Visual Comput Graph , 29 ( 1 ): 734 - 744 . doi: 10.1109/TVCG.2022.3209423 http://doi.org/10.1109/TVCG.2022.3209423

Yalcin MA , Elmqvist N , Bederson BB , 2016 . AggreSet: rich and scalable set exploration using visualizations of element aggregations . IEEE Trans Visual Comput Graph , 22 ( 1 ): 688 - 697 . doi: 10.1109/TVCG.2015.2467051 http://doi.org/10.1109/TVCG.2015.2467051

Yuan J , Chen CJ , Yang WK , et al. , 2021 . A survey of visual analytics techniques for machine learning . Comput Visual Med , 7 ( 1 ): 3 - 36 . doi: 10.1007/s41095-020-0191-7 http://doi.org/10.1007/s41095-020-0191-7

Yuan QM , Zhang H , 2022 . Research on the characteristics of light sources in machine vision . Acad J Sci Technol , 3 ( 1 ): 1 - 6 . doi: 10.54097/ajst.v3i1.1655 http://doi.org/10.54097/ajst.v3i1.1655

Zhu ZH , Shen Y , Zhu SJ , et al. , 2022 . Towards better pattern enhancement in temporal evolving set visualization . J Vis , early access . doi: 10.1007/s12650-022-00896-x http://doi.org/10.1007/s12650-022-00896-x

Zorcolo A , Escobar-Palafox G , Gault R , et al. , 2011 . Study of lighting solutions in machine vision applications for automated assembly operations . IOP Conf Ser Mater Sci Eng , 26 ( 1 ): 012019 . doi: 10.1088/1757-899X/26/1/012019 http://doi.org/10.1088/1757-899X/26/1/012019

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