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Rotation Estimation and Segmentation for Patterned Image Vision Inspection

Authors
Oh, CheoninKim, HyungwooCho, Hyeonjoong
Issue Date
Dec-2021
Publisher
MDPI
Keywords
rotation estimation; pattern image segmentation; vision inspection; fabric defect detection; segmentation reference point
Citation
ELECTRONICS, v.10, no.23
Indexed
SCIE
SCOPUS
Journal Title
ELECTRONICS
Volume
10
Number
23
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/139528
DOI
10.3390/electronics10233040
ISSN
2079-9292
2079-9292
Abstract
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.
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