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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 [1]
2019 [2]
2012 [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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Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features
期刊论文
Remote Sensing, 2022, 卷号: 14, 期号: 14, 页码: 28
作者:
Y. Tian
;
J. H. Liu
;
S. J. Zhu
;
F. Xu
;
G. B. Bai and C. L. Liu
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浏览/下载:1/0
  |  
提交时间:2023/06/14
The Cat's Eye Effect Target Recognition Method Based on Visual Attention
期刊论文
Chinese Journal of Electronics, 2019, 卷号: 28, 期号: 5, 页码: 1080-1086
作者:
X.B.Wang
;
J.Zhang
;
S.H.Wang
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  |  
浏览/下载:1/0
  |  
提交时间:2020/08/24
feature extraction,image fusion,object detection,target-saliency map,target region,Cat's eye effect target recognition method,visual,attention,second-directional derivative filter,directional channels,morphological method,potential targets exist,designed saliency fusing,method,candidate targets,Cat's eye effect target recognition method,based on visual attention (CTRVA),Target recognition,Saliency map,Engineering
The Cat's Eye Effect Target Recognition Method Based on Visual Attention
期刊论文
Chinese Journal of Electronics, 2019, 卷号: 28, 期号: 5, 页码: 1080-1086
作者:
X.B.Wang
;
J.Zhang
;
S.H.Wang
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2020/08/24
feature extraction,image fusion,object detection,target-saliency map,target region,Cat's eye effect target recognition method,visual,attention,second-directional derivative filter,directional channels,morphological method,potential targets exist,designed saliency fusing,method,candidate targets,Cat's eye effect target recognition method,based on visual attention (CTRVA),Target recognition,Saliency map,Engineering
Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE)
会议论文
2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China
Wang X.
;
Li H.
;
Dong N.
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  |  
浏览/下载:22/0
  |  
提交时间:2013/03/25
Using of SIFT algorithm in the image of teeth model
can detect the features of the teeth image effectively. In this approach
first
search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second
select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third
assign one or more orientations to each keypoint location based on local image gradient directions. Last
measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods
this method can detect the features of the teeth model effectively
and offer some available parameters for 3D reconstruction of the teeth model. the authors.
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