Daniel Munoz paper Contextual Classification with Functional Max-Margin Markov Networks

struct_predict_smooth_v1.342\0README.txt
............................\CMakeLists.txt
............................\m3n\CMakeLists.txt
............................\...\examples\example.cc
............................\...\........\random_field\train_rf.cs_0_features
............................\...\........\............\train_rf.cs_1_features
............................\...\........\............\train_rf.node_features
............................\...\........\............\train_rf.random_field
............................\...\........\............\train_rf_point_cloud.wrl
............................\...\include\m3n\functional_m3n.h
............................\...\.......\...\m3n_model.h
............................\...\.......\...\parametric_m3n.h
............................\...\.......\...\random_field.h
............................\...\.......\...\.egressors\linear_regression.h
............................\...\.......\...\..........\ocv_rtree_wrapper.h
............................\...\.......\...\..........\regressor_includes.h
............................\...\.......\...\..........\regressor_wrapper.h
............................\...\.......\...\util\m3n_logging.h
............................\...\.......\...\....\m3n_stats.h
............................\...\Makefile
............................\...\manifest.xml
............................\...\src\functional_m3n.cc
............................\...\...\m3n_model.cc
............................\...\...\m3n_model_inference.cc
............................\...\...\m3n_model_learning.cc
............................\...\...\parametric_m3n.cc
............................\...\...\random_field.cc
............................\...\...\random_field_io_wg.cc
............................\...\...\.egressors\linear_regression.cc
............................\...\...\..........\ocv_rtree_wrapper.cc
............................\...\...\..........\regressor_wrapper.cc
............................\submodular_graphcut\CMakeLists.txt
............................\...................\include\submodular_graphcut\graphcut_bgl.h
............................\...................\.......\...................\graphcut_vk3.h
............................\...................\.......\...................\submodular_graphcut.h
............................\...................\Makefile
............................\...................\manifest.xml
............................\...................\src\graphcut_bgl.cc
............................\...................\...\submodular_graphcut.cc
............................\m3n\include\m3n\regressors
............................\...\.......\...\util
............................\...\examples\learned_m3n_model
............................\...\........\random_field
............................\...\........\resaved_m3n_model
............................\...\include\m3n
............................\...\src\regressors
............................\submodular_graphcut\include\submodular_graphcut
............................\m3n\examples
............................\...\include
............................\...\src
............................\submodular_graphcut\include
............................\...................\src
............................\m3n
............................\submodular_graphcut
struct_predict_smooth_v1.342

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．