Rotation Estimation and Segmentation for Patterned Image Vision Inspection
Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceed...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/23/3040 |
| _version_ | 1797507935959515136 |
|---|---|
| author | Cheonin Oh Hyungwoo Kim Hyeonjoong Cho |
| author_facet | Cheonin Oh Hyungwoo Kim Hyeonjoong Cho |
| author_sort | Cheonin Oh |
| collection | DOAJ |
| description | Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern. |
| first_indexed | 2024-03-10T04:55:30Z |
| format | Article |
| id | doaj.art-a1253539c83c449b9f1f063b59dea1de |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:55:30Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a1253539c83c449b9f1f063b59dea1de2023-11-23T02:18:08ZengMDPI AGElectronics2079-92922021-12-011023304010.3390/electronics10233040Rotation Estimation and Segmentation for Patterned Image Vision InspectionCheonin Oh0Hyungwoo Kim1Hyeonjoong Cho2Intelligent Convergence Research Laboratory, ETRI, Deajeon 34129, KoreaMachine Vision Research Team, Bluetilelab, Seongnam 13529, KoreaDepartment of Computer and Information Science, Korea University, Sejong 30019, KoreaPattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern.https://www.mdpi.com/2079-9292/10/23/3040rotation estimationpattern image segmentationvision inspectionfabric defect detectionsegmentation reference point |
| spellingShingle | Cheonin Oh Hyungwoo Kim Hyeonjoong Cho Rotation Estimation and Segmentation for Patterned Image Vision Inspection Electronics rotation estimation pattern image segmentation vision inspection fabric defect detection segmentation reference point |
| title | Rotation Estimation and Segmentation for Patterned Image Vision Inspection |
| title_full | Rotation Estimation and Segmentation for Patterned Image Vision Inspection |
| title_fullStr | Rotation Estimation and Segmentation for Patterned Image Vision Inspection |
| title_full_unstemmed | Rotation Estimation and Segmentation for Patterned Image Vision Inspection |
| title_short | Rotation Estimation and Segmentation for Patterned Image Vision Inspection |
| title_sort | rotation estimation and segmentation for patterned image vision inspection |
| topic | rotation estimation pattern image segmentation vision inspection fabric defect detection segmentation reference point |
| url | https://www.mdpi.com/2079-9292/10/23/3040 |
| work_keys_str_mv | AT cheoninoh rotationestimationandsegmentationforpatternedimagevisioninspection AT hyungwookim rotationestimationandsegmentationforpatternedimagevisioninspection AT hyeonjoongcho rotationestimationandsegmentationforpatternedimagevisioninspection |