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Lvhan PAN, Guodao SUN, Baofeng CHANG, Wang XIA, Qi JIANG, Jingwei TANG, Ronghua LIANG. Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement",
author="Lvhan PAN, Guodao SUN, Baofeng CHANG, Wang XIA, Qi JIANG, Jingwei TANG, Ronghua LIANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200547"
}
%0 Journal Article
%T Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement
%A Lvhan PAN
%A Guodao SUN
%A Baofeng CHANG
%A Wang XIA
%A Qi JIANG
%A Jingwei TANG
%A Ronghua LIANG
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200547
TY - JOUR
T1 - Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement
A1 - Lvhan PAN
A1 - Guodao SUN
A1 - Baofeng CHANG
A1 - Wang XIA
A1 - Qi JIANG
A1 - Jingwei TANG
A1 - Ronghua LIANG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200547
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 the traditional workflow, engineers constantly adjust and verify the configuration for an acceptable result, which is time-consuming and significantly depends on professional 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: a data preparation stage, a data sampling stage, a data processing stage 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 relationship among images using the algorithmic parameters. Based on the image relationship, we developed 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 conducted case studies with engineers and obtained feedback from expert interviews.
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