상세정보
학술지
Rotation Estimation and Segmentation for Patterned Image Vision Inspection
- 저자
- 오천인, 김형우, 조현중
- 발행일
- 202112
- 출처
- Electronics, v.10 no.23, pp.1-20
- ISSN
- 2079-9292
- 출판사
- MDPI
- 초록
- 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.
- KSP 제안 키워드
- Conventional methods, Defect Detection, Distance map, Estimation method, Fabric defects, Inspection time, Orientation vector, Reference point, Segmented images, Separation distance, Striped pattern
본 저작물은 크리에이티브 커먼즈 저작자 표시 (CC BY) 조건에 따라 이용할 수 있습니다.
