The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
지능형 비전 측정을 위해서는 기하학적 이미지 특징 추출이 필수적인 문제입니다. CPI(Contour Prinative of Interest)는 대상 물체 위에 놓인 규칙적인 모양의 윤곽 특징을 의미하며 비전 측정 및 서보잉의 기하학적 계산에 널리 사용됩니다. CPI 추출 모델이 다양한 새로운 객체에 유연하게 적용될 수 있다는 것을 실현하기 위해 단 하나의 주석이 달린 지원 이미지를 사용하여 CPI 추출 프로세스를 안내함으로써 심층 컨벌루션 신경망을 사용하여 원샷 학습 기반 CPI 추출을 구현할 수 있습니다. 본 논문에서는 다단계 전략을 사용하여 CPI와 복잡한 배경의 식별 능력을 향상시키는 다단계 관심 윤곽 프리미티브 추출 네트워크(MS-CPieNet)를 제안합니다. 둘째, 공간적 비지역 주의 모듈은 단거리 및 장거리 모두의 이미지 특징을 전역적으로 융합하여 깊은 특징을 향상시키는 데 활용됩니다. 또한, 밀집된 4방향 분류는 윤곽선의 법선 방향을 얻기 위해 설계되었으며, 이 방향은 윤곽선 세선화 후 처리에 추가로 사용될 수 있습니다. 제안된 방법의 효율성은 OCP 및 ROCM 데이터 세트를 사용한 실험을 통해 검증되었습니다. 제안된 MS-CPieNet의 편리한 적용을 입증하기 위해 2차원 측정 실험을 수행한다.
Jinyan LU
Henan University of Engineering
Quanzhen HUANG
Henan University of Engineering
Shoubing LIU
Henan University of Engineering
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부
Jinyan LU, Quanzhen HUANG, Shoubing LIU, "Multi-Stage Contour Primitive of Interest Extraction Network with Dense Direction Classification" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 10, pp. 1743-1750, October 2022, doi: 10.1587/transinf.2022EDP7031.
Abstract: For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7031/_p
부
@ARTICLE{e105-d_10_1743,
author={Jinyan LU, Quanzhen HUANG, Shoubing LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Stage Contour Primitive of Interest Extraction Network with Dense Direction Classification},
year={2022},
volume={E105-D},
number={10},
pages={1743-1750},
abstract={For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.},
keywords={},
doi={10.1587/transinf.2022EDP7031},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Multi-Stage Contour Primitive of Interest Extraction Network with Dense Direction Classification
T2 - IEICE TRANSACTIONS on Information
SP - 1743
EP - 1750
AU - Jinyan LU
AU - Quanzhen HUANG
AU - Shoubing LIU
PY - 2022
DO - 10.1587/transinf.2022EDP7031
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2022
AB - For intelligent vision measurement, the geometric image feature extraction is an essential issue. Contour primitive of interest (CPI) means a regular-shaped contour feature lying on a target object, which is widely used for geometric calculation in vision measurement and servoing. To realize that the CPI extraction model can be flexibly applied to different novel objects, the one-shot learning based CPI extraction can be implemented with deep convolutional neural network, by using only one annotated support image to guide the CPI extraction process. In this paper, we propose a multi-stage contour primitives of interest extraction network (MS-CPieNet), which uses the multi-stage strategy to improve the discrimination ability of CPI and complex background. Second, the spatial non-local attention module is utilized to enhance the deep features, by globally fusing the image features with both short and long ranges. Moreover, the dense 4-direction classification is designed to obtain the normal direction of the contour, and the directions can be further used for the contour thinning post-process. The effectiveness of the proposed methods is validated by the experiments with the OCP and ROCM datasets. A 2-D measurement experiments are conducted to demonstrate the convenient application of the proposed MS-CPieNet.
ER -