文件名称:PartsBasedDetector-master
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2016-01-07
- 文件大小：
- 184kb
- 下载次数：
- 0次
- 提 供 者：
- hong****
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
人体姿势识别 可以识别出人体的14个节点 连接节点然后得出姿势图-
人体姿势识别 可以识别出人体的14个节点 连接节点然后得出姿势图
Human body posture recognition can identify the body of the 14 nodes connected to the node and then draw a picture
人体姿势识别 可以识别出人体的14个节点 连接节点然后得出姿势图
Human body posture recognition can identify the body of the 14 nodes connected to the node and then draw a picture
(系统自动生成,下载前可以参看下载内容)
下载文件列表
PartsBasedDetector-master\.gitmodules
.........................\cells\CMakeLists.txt
.........................\.....\detect.cpp
.........................\.....\module.cpp
.........................\.make\FindEigen.cmake
.........................\CMakeLists.txt
.........................\conf\config_face.by_parts
.........................\....\config_person.by_parts
.........................\doc\Doxyfile.in
.........................\include\Candidate.hpp
.........................\.......\DepthConsistency.hpp
.........................\.......\DistanceTransform.hpp
.........................\.......\DynamicProgram.hpp
.........................\.......\FileStorageModel.hpp
.........................\.......\FourierConvolutionEngine.hpp
.........................\.......\HOGFeatures.hpp
.........................\.......\IConvolutionEngine.hpp
.........................\.......\IFeatures.hpp
.........................\.......\Math.hpp
.........................\.......\MatlabIOModel.hpp
.........................\.......\Metrics.hpp
.........................\.......\Model.hpp
.........................\.......\nms.hpp
.........................\.......\Parts.hpp
.........................\.......\PartsBasedDetector.hpp
.........................\.......\PointCloudClusterer.h
.........................\.......\PointCloudClusterer.hpp
.........................\.......\Rect3.hpp
.........................\.......\SearchSpacePruning.hpp
.........................\.......\SpatialConvolutionEngine.hpp
.........................\.......\StereoCameraModel.hpp
.........................\.......\types.hpp
.........................\.......\Visualize.hpp
.........................\INSTALL
.........................\launch\detect.launch
.........................\matlab\compile.m
.........................\......\detection\bestoverlap.m
.........................\......\.........\detect.m
.........................\......\.........\detect_fast.m
.........................\......\.........\featpyramid.m
.........................\......\.........\nms.m
.........................\......\.........\testmodel.m
.........................\......\.........\testmodel_gtbox.m
.........................\......\evaluation\eval_apk.m
.........................\......\..........\eval_pck.m
.........................\......\..........\VOCap.m
.........................\......\globals.m
.........................\......\isoctave.m
.........................\......\learning\annotateParts.m
.........................\......\........\buildmodel.m
.........................\......\........\clusterparts.m
.........................\......\........\clusterparts_poselet.m
.........................\......\........\clusterparts_vis.m
.........................\......\........\croppos.m
.........................\......\........\data_def.m
.........................\......\........\fprintflush.m
.........................\......\........\getNegativeData.m
.........................\......\........\getPositiveData.m
.........................\......\........\initmodel.m
.........................\......\........\k_means.m
.........................\......\........\map_rotate_points.m
.........................\......\........\mergemodels.m
.........................\......\........\model2vec.m
.........................\......\........\point2box.m
.........................\......\........\pointtobox.m
.........................\......\........\qp_one.m
.........................\......\........\qp_opt.m
.........................\......\........\qp_prune.m
.........................\......\........\qp_refresh.m
.........................\......\........\qp_w.m
.........................\......\........\qp_write.m
.........................\......\........\sparse2dense.m
.........................\......\........\subarray.m
.........................\......\........\train.m
.........................\......\........\trainmodel.m
.........................\......\........\vec2model.m
.........................\......\........\warppos.m
.........................\......\LICENSE
.........................\......\mex\dt.cc
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