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GMS-Feature-Matcher-master

于 2021-05-06 发布
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下载积分: 1 下载次数: 2

代码说明:

说明:  利用GMS对特征点匹配进行约束,降低误匹配率,代码包含python版本,C++版本,matlab版本。(GMS is used to constrain the matching of feature points to reduce the error matching rate. The code includes Python version, C + + version and Matlab version.)

文件列表:

GMS-Feature-Matcher-master, 0 , 2020-06-10
GMS-Feature-Matcher-master\CMakeLists.txt, 270 , 2020-06-10
GMS-Feature-Matcher-master\LICENSE, 1512 , 2020-06-10
GMS-Feature-Matcher-master\README.md, 3306 , 2020-06-10
GMS-Feature-Matcher-master\data, 0 , 2020-06-10
GMS-Feature-Matcher-master\data\01.jpg, 63193 , 2020-06-10
GMS-Feature-Matcher-master\data\02.jpg, 37640 , 2020-06-10
GMS-Feature-Matcher-master\data\result_dogs_colorcode.png, 428540 , 2020-06-10
GMS-Feature-Matcher-master\data\result_dogs_lines.png, 387141 , 2020-06-10
GMS-Feature-Matcher-master\include, 0 , 2020-06-10
GMS-Feature-Matcher-master\include\gms_matcher.h, 8899 , 2020-06-10
GMS-Feature-Matcher-master\matlab, 0 , 2020-06-10
GMS-Feature-Matcher-master\matlab\Compile.m, 501 , 2020-06-10
GMS-Feature-Matcher-master\matlab\MexGMS.cpp, 2260 , 2020-06-10
GMS-Feature-Matcher-master\matlab\demo.m, 297 , 2020-06-10
GMS-Feature-Matcher-master\matlab\gms_match.m, 462 , 2020-06-10
GMS-Feature-Matcher-master\python, 0 , 2020-06-10
GMS-Feature-Matcher-master\python\gms_matcher.py, 13958 , 2020-06-10
GMS-Feature-Matcher-master\python\opencv_demo.py, 3562 , 2020-06-10
GMS-Feature-Matcher-master\src, 0 , 2020-06-10
GMS-Feature-Matcher-master\src\demo.cpp, 2834 , 2020-06-10

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    2010-11-09 10:51:46下载
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    采用了Harrtraining和离子滤波算法实现了拥挤人群人头计数,非常有用。VC++和Opencv开发,采用Oepncv中的Harrtraining训练一个分类器即可运行(Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framework, and in- troduce a con rmation-by-classi cation method to estimate con dence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections.)
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    基于SUFT的特征提取与匹配,内涵详细的文档说明,很好的实现了特征提取与匹配,该方法较基本的SIFT有很大的提高(Extraction and matching of features based on SUFT, detailed content of the documentation, very good realization of the feature extraction and matching, SIFT the method is basic is greatly improved)
    2013-10-04 11:10:04下载
    积分:1
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    2013-06-25 09:30:57下载
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