文件名称:hmm_matlab

  • 所属分类:
  • 图形/文字识别
  • 资源属性:
  • [Matlab] [源码]
  • 上传时间:
  • 2013-04-12
  • 文件大小:
  • 3.84mb
  • 下载次数:
  • 0次
  • 提 供 者:
  • 王**
  • 相关连接:
  • 下载说明:
  • 别用迅雷下载,失败请重下,重下不扣分!

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基于隐马尔科夫模型HMM的人脸识别程序,使用Matlab编写,提取的人脸识别特征为DCT系数-it is face detection codes based on HMM Model ,you can run iton Matlab,the feature from face is DCT
(系统自动生成,下载前可以参看下载内容)

下载文件列表





hmm_matlab

..........\dct_hmm.m

..........\dct_test1.m

..........\em_converged.m

..........\fwdback.m

..........\isposdef.m

..........\KPMstats

..........\........\#histCmpChi2.m#

..........\........\beta_sample.m

..........\........\chisquared_histo.m

..........\........\chisquared_prob.m

..........\........\chisquared_readme.txt

..........\........\chisquared_table.m

..........\........\clg_Mstep.m

..........\........\clg_Mstep_simple.m

..........\........\clg_prob.m

..........\........\condGaussToJoint.m

..........\........\condgaussTrainObserved.m

..........\........\condgauss_sample.m

..........\........\cond_indep_fisher_z.m

..........\........\convertBinaryLabels.m

..........\........\cwr_demo.m

..........\........\cwr_em.m

..........\........\cwr_predict.m

..........\........\cwr_prob.m

..........\........\cwr_readme.txt

..........\........\cwr_test.m

..........\........\dirichletpdf.m

..........\........\dirichletrnd.m

..........\........\dirichlet_sample.m

..........\........\distchck.m

..........\........\eigdec.m

..........\........\est_transmat.m

..........\........\fit_paritioned_model_testfn.m

..........\........\fit_partitioned_model.m

..........\........\fwdback.m

..........\........\gamma_sample.m

..........\........\gaussian_prob.asv

..........\........\gaussian_prob.m

..........\........\gaussian_sample.m

..........\........\histCmpChi2.m

..........\........\histCmpChi2.m~

..........\........\KLgauss.m

..........\........\linear_regression.m

..........\........\logist2.m

..........\........\logist2Apply.m

..........\........\logist2ApplyRegularized.m

..........\........\logist2Fit.m

..........\........\logist2FitRegularized.m

..........\........\logistK.m

..........\........\logistK_eval.m

..........\........\marginalize_gaussian.m

..........\........\matrix_normal_pdf.m

..........\........\matrix_T_pdf.m

..........\........\mc_stat_distrib.m

..........\........\mhmm_em.m

..........\........\mhmm_logprob.m

..........\........\mixgauss_classifier_apply.m

..........\........\mixgauss_classifier_train.m

..........\........\mixgauss_em.m

..........\........\mixgauss_init.m

..........\........\mixgauss_Mstep.m

..........\........\mixgauss_prob.asv

..........\........\mixgauss_prob.m

..........\........\mixgauss_prob_test.m

..........\........\mixgauss_sample.m

..........\........\mkPolyFvec.m

..........\........\mk_unit_norm.m

..........\........\multinomial_prob.m

..........\........\multinomial_sample.m

..........\........\multipdf.m

..........\........\multirnd.m

..........\........\normal_coef.m

..........\........\partial_corr_coef.m

..........\........\parzen.m

..........\........\parzenC.c

..........\........\parzenC.dll

..........\........\parzenC.mexglx

..........\........\parzenC_test.m

..........\........\parzen_fit_select_unif.m

..........\........\pca.m

..........\........\README.txt

..........\........\rndcheck.m

..........\........\sample.m

..........\........\sample_discrete.m

..........\........\sample_gaussian.m

..........\........\standardize.m

..........\........\standardize.m~

..........\........\student_t_logprob.m

..........\........\student_t_prob.m

..........\........\test_dir.m

..........\........\unidrndKPM.m

..........\........\unidrndKPM.m~

..........\........\unif_discrete_sample.m

..........\........\viterbi_path.m

..........\........\weightedRegression.m

..........\KPMtools

..........\........\approxeq.m

..........\........\approx_unique.m

..........\........\argmax.m

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