文件名称:MDL_segmenter

  • 所属分类:
  • matlab例程
  • 资源属性:
  • [Matlab] [源码]
  • 上传时间:
  • 2012-11-26
  • 文件大小:
  • 121kb
  • 下载次数:
  • 0次
  • 提 供 者:
  • kav****
  • 相关连接:
  • 下载说明:
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em algorithm - Find approximate solution to Sf = conv(s,f) = d using EM iteration.

EM seeks to minimize the Poisson negative log likelihood function

J(f) = sum_i {[Sf]_i - (d_i + sigma^2)*log([Sf]_i + sigma^2)}.

-em algorithm - Find approximate solution to Sf = conv(s,f) = d using EM iteration.

EM seeks to minimize the Poisson negative log likelihood function

  J(f) = sum_i {[Sf]_i - (d_i + sigma^2)*log([Sf]_i + sigma^2)}.


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下载文件列表

MDL_segmenter\coding_length.m

.............\coding_length_v1.m

.............\coding_length_v2.m

.............\gaussian2D.tif

.............\MDL_segmentor.asv

.............\MDL_segmentor.m

.............\subspaces3D.tif

.............\test_MDL_segmentor.asv

.............\test_MDL_segmentor.m

.............\helper_functions

.............\................\.DS_Store

.............\................\._.DS_Store

.............\................\arrow.m

.............\................\arrow3.m

.............\................\average_basis_error.m

.............\................\balls_and_bins.m

.............\................\cluster_subspaces.m

.............\................\EM_subspace.m

.............\................\find_good_sample.m

.............\................\generate_group_dimension_configurations.m

.............\................\generate_hilbert_constraint_entry.m

.............\................\generate_image_noise.m

.............\................\generate_samples.asv

.............\................\generate_samples.m

.............\................\generate_samples_for_hilbert_constraint.m

.............\................\generate_subsets.m

.............\................\generate_veronese_maps.m

.............\................\hat.m

.............\................\hypersphere_area.m

.............\................\hypersphere_volume.m

.............\................\intersection_reclassification.m

.............\................\Ksubspaces.m

.............\................\K_means_subspaces_fixed_dimension.m

.............\................\mahalanobis_distance.m

.............\................\mst.m

.............\................\normalize.m

.............\................\normalize_variance.m

.............\................\PCA_downsampler.m

.............\................\plot_data.m

.............\................\plot_level_plane.m

.............\................\plot_normals.m

.............\................\plot_polynomial.m

.............\................\plot_subspaces.m

.............\................\plot_subspaces2.m

.............\................\plot_subspaces3.asv

.............\................\plot_subspaces3.m

.............\................\plot_subspaces4.m

.............\................\plot_unit_sphere.m

.............\................\point_to_space_distance.m

.............\................\rand_hypersphere.m

.............\................\rand_special_orthogonal.m

.............\................\rand_uniform_inside_hypersphere.m

.............\................\rand_uniform_on_hypersphere.m

.............\................\relabel_samples.m

.............\................\relabel_samples_greedy.asv

.............\................\relabel_samples_greedy.m

.............\................\stereo_view.m

.............\................\subspace_angle.m

.............\................\subspace_bases.m

.............\................\subspace_voting.m

.............\................\test_EM_subspaces.m

.............\................\test_hypersphere_area.m

.............\................\test_hypersphere_volume.m

.............\................\test_Ksubspaces.m

.............\................\test_normalize_variation.m

.............\................\test_rand_special_orthogonal.m

.............\................\test_rand_uniform_inside_hypersphere.m

.............\................\test_rand_uniform_on_hypersphere.m

.............\................\test_rgpca_leave_one_out.m

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