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
본 논문에서는 다중 특징 융합과 에지 제약을 이용한 돌출 영역 검출 방법을 제안한다. 먼저, 조밀한 연결 구조와 다중 채널 컨볼루션 채널을 기반으로 한 이미지 특징 추출 및 융합 네트워크를 설계합니다. 그런 다음 수신 필드를 확대하기 위해 다중 규모 심방 컨볼루션 블록을 적용합니다. 마지막으로 정확도를 높이기 위해 다중 작업 훈련을 위해 분류된 손실과 에지 손실을 포함하는 결합된 손실 함수가 구축되었습니다. 실험 결과는 제안된 방법의 유효성을 검증한다.
Cheng XU
Nanjing University of Aeronautics and Astronautics
Wei HAN
Nanjing University of Aeronautics and Astronautics
Dongzhen WANG
Nanjing University of Aeronautics and Astronautics
Daqing HUANG
Nanjing University of Aeronautics and Astronautics
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부
Cheng XU, Wei HAN, Dongzhen WANG, Daqing HUANG, "Salient Region Detection with Multi-Feature Fusion and Edge Constraint" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 910-913, April 2020, doi: 10.1587/transinf.2019EDL8181.
Abstract: In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8181/_p
부
@ARTICLE{e103-d_4_910,
author={Cheng XU, Wei HAN, Dongzhen WANG, Daqing HUANG, },
journal={IEICE TRANSACTIONS on Information},
title={Salient Region Detection with Multi-Feature Fusion and Edge Constraint},
year={2020},
volume={E103-D},
number={4},
pages={910-913},
abstract={In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.},
keywords={},
doi={10.1587/transinf.2019EDL8181},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Salient Region Detection with Multi-Feature Fusion and Edge Constraint
T2 - IEICE TRANSACTIONS on Information
SP - 910
EP - 913
AU - Cheng XU
AU - Wei HAN
AU - Dongzhen WANG
AU - Daqing HUANG
PY - 2020
DO - 10.1587/transinf.2019EDL8181
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.
ER -