Application of machine vision technology in geometric dimension measurement of small parts
Abstract In this paper, the on-line detection of small parts’ dimension measurement based on machine vision is designed, and the key technologies, such as image processing, image registration and stitching, edge detection, sub-pixel location analysis, image feature recognition and clustering, and sc...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-11-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13640-018-0364-9 |
| _version_ | 1831645515362598912 |
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| author | Bin Li |
| author_facet | Bin Li |
| author_sort | Bin Li |
| collection | DOAJ |
| description | Abstract In this paper, the on-line detection of small parts’ dimension measurement based on machine vision is designed, and the key technologies, such as image processing, image registration and stitching, edge detection, sub-pixel location analysis, image feature recognition and clustering, and scale measurement based on image involved in the detection of small part dimension are studied. Firstly, based on the actual usage and the characteristics of the algorithm, the feature-based SIFT algorithm was selected to complete the image registration, and the image edge detection algorithm and data processing method were explored. The histogram equalization improves the grayscale distribution of the stitched image and improves the contrast of the image. The median noise filtering algorithm was used to complete the image noise reduction. The false edge was filtered to get the single pixel edge, and the least square method was used to compensate the missing edge pixels and reduce the measurement error. An accurate image registration transformation matrix was obtained. Then, the weighted average fusion algorithm was used to complete the image fusion. The experimental results show that the image stitching algorithm is accurate and effective, and the measurement accuracy of the system meets the performance requirements. |
| first_indexed | 2024-12-19T13:37:54Z |
| format | Article |
| id | doaj.art-dab127f67c6943f19219f29182e4ffb8 |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-12-19T13:37:54Z |
| publishDate | 2018-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-dab127f67c6943f19219f29182e4ffb82022-12-21T20:19:08ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812018-11-01201811810.1186/s13640-018-0364-9Application of machine vision technology in geometric dimension measurement of small partsBin Li0School of Mechanical Engineering, Tianjin University of Technology and EducationAbstract In this paper, the on-line detection of small parts’ dimension measurement based on machine vision is designed, and the key technologies, such as image processing, image registration and stitching, edge detection, sub-pixel location analysis, image feature recognition and clustering, and scale measurement based on image involved in the detection of small part dimension are studied. Firstly, based on the actual usage and the characteristics of the algorithm, the feature-based SIFT algorithm was selected to complete the image registration, and the image edge detection algorithm and data processing method were explored. The histogram equalization improves the grayscale distribution of the stitched image and improves the contrast of the image. The median noise filtering algorithm was used to complete the image noise reduction. The false edge was filtered to get the single pixel edge, and the least square method was used to compensate the missing edge pixels and reduce the measurement error. An accurate image registration transformation matrix was obtained. Then, the weighted average fusion algorithm was used to complete the image fusion. The experimental results show that the image stitching algorithm is accurate and effective, and the measurement accuracy of the system meets the performance requirements.http://link.springer.com/article/10.1186/s13640-018-0364-9Machine visionSmall partsEdge detectionImage feature recognition |
| spellingShingle | Bin Li Application of machine vision technology in geometric dimension measurement of small parts EURASIP Journal on Image and Video Processing Machine vision Small parts Edge detection Image feature recognition |
| title | Application of machine vision technology in geometric dimension measurement of small parts |
| title_full | Application of machine vision technology in geometric dimension measurement of small parts |
| title_fullStr | Application of machine vision technology in geometric dimension measurement of small parts |
| title_full_unstemmed | Application of machine vision technology in geometric dimension measurement of small parts |
| title_short | Application of machine vision technology in geometric dimension measurement of small parts |
| title_sort | application of machine vision technology in geometric dimension measurement of small parts |
| topic | Machine vision Small parts Edge detection Image feature recognition |
| url | http://link.springer.com/article/10.1186/s13640-018-0364-9 |
| work_keys_str_mv | AT binli applicationofmachinevisiontechnologyingeometricdimensionmeasurementofsmallparts |