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长春光学精密机械与物... [4]
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期刊论文 [3]
会议论文 [1]
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2022 [3]
2010 [1]
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专题:长春光学精密机械与物理研究所
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Vehicle pressure line detection based on improved Mask R-CNN + LaneNet
期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2022, 卷号: 30, 期号: 7, 页码: 854-868
作者:
J. Sun
;
Y. Zhang and X. Chang
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提交时间:2023/06/14
Vehicle pressure line detection based on improved Mask R-CNN + LaneNet
期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2022, 卷号: 30, 期号: 7, 页码: 854-868
作者:
J. Sun
;
Y. Zhang and X. Chang
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提交时间:2023/07/14
Colorful Image Colorization with Classification and Asymmetric Feature Fusion
期刊论文
Sensors, 2022, 卷号: 22, 期号: 20, 页码: 16
作者:
Z. Y. Wang
;
Y. Yu
;
D. Q. Li
;
Y. Y. Wan and M. Y. Li
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提交时间:2023/06/14
A new method of target recognition based on rough set and support vector machine (EI CONFERENCE)
会议论文
2nd International Conference on Image Analysis and Signal Processing, IASP'2010, April 12, 2010 - April 14, 2010, Xiamen, China
Guo Z.-J.
;
He X.
;
Wei Z.-H.
;
Liang G.-L.
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提交时间:2013/03/25
Automatic target recognition (ATR) is an important task in image application. This paper concentrates on two key subroutines of ATR system: Pre-treatment and design of classifier. In the pre-treatment subroutine
a new method based on Rough Set (RS) is proposed to partition the original sample set into some subsets and calculate their class membership
so that some samples can be chosen by class membership to be trained. After pre-treatment
an iterative algorithm based on Rough Set and Support Vector Machines (IRSSVM) is introduced to design a classifier for recognizing two types of targets. The experiment results show that IRSSVM needs less training time and the classifier is simpler and has more generalization and higher recognition rate. 2010 IEEE.
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