文件列表:

fdtool (0, 2010-11-22)
fdtool\0000_-12_0_0_15_0_1.pgm (40015, 2005-06-16)
fdtool\2.bmp (1469286, 2008-09-18)
fdtool\basicroc.m (1583, 2009-05-11)
fdtool\build_negatives.m (1718, 2009-05-13)
fdtool\chlbp.c (21318, 2010-11-09)
fdtool\chlbp_adaboost_binary_model_cascade.c (12474, 2009-04-25)
fdtool\chlbp_adaboost_binary_predict_cascade.c (12030, 2009-05-19)
fdtool\chlbp_gentleboost_binary_model_cascade.c (17974, 2010-11-21)
fdtool\chlbp_gentleboost_binary_predict_cascade.c (12735, 2009-05-19)
fdtool\class57.jpg (174549, 2008-09-09)
fdtool\d2uint8_image.m (115, 2009-03-09)
fdtool\demo_chlbp.m (12204, 2009-05-09)
fdtool\demo_detector_haar.m (2898, 2010-11-21)
fdtool\demo_haar.m (16654, 2009-06-21)
fdtool\demo_mblbp.m (21392, 2009-11-09)
fdtool\detector_haar.c (42918, 2010-11-21)
fdtool\detector_mblbp.c (33522, 2010-11-21)
fdtool\display_database.m (748, 2009-05-09)
fdtool\eval_chlbp.c (27041, 2010-11-21)
fdtool\eval_haar.c (23661, 2010-11-09)
fdtool\eval_haar_subwindow.c (22340, 2010-11-21)
fdtool\eval_mblbp.c (17689, 2010-11-21)
fdtool\fast_haar_adaboost_binary_model_cascade.c (22219, 2009-06-09)
fdtool\fast_haar_ada_weaklearner.c (15900, 2009-11-12)
fdtool\generate_data.m (7485, 2009-06-21)
fdtool\getmapping.m (2757, 2009-01-18)
fdtool\gui (0, 2009-12-02)
fdtool\gui\coeff_edit_data_callback1.m (171, 2009-04-20)
fdtool\gui\coeff_edit_data_callback10.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback11.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback12.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback13.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback14.m (171, 2009-04-20)
fdtool\gui\coeff_edit_data_callback15.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback16.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback17.m (171, 2009-04-20)
fdtool\gui\coeff_edit_data_callback18.m (171, 2009-04-20)
fdtool\gui\coeff_edit_data_callback19.m (173, 2009-04-20)
fdtool\gui\coeff_edit_data_callback2.m (169, 2009-04-20)
... ...

Faces detection toolbox v 0.1e ------------------------------ This toolbox provides some tools for faces detection using Local Binary Patterns and Haar features. The task of detection is done by boosting approaches such Adaboosting, FastAdaboosting and Gentleboosting. The main objective of this toolbox is to deliver simple but efficient tools mainly written in C codes with a matlab interface and easy to modify. Installation ------------ This toolbox has been tested on Windows system and should work also on Linux plateform without any problem. Please run "mexme_fdt" to compile each mex-files and add fdtool directory in the matlab path. Please open *.c files to have a description of each function and how to use them in Matlab. In order to compile vcapg2.cpp (webcam grabber for windows), please i)download the last Windows DDK at http://www.microsoft.com/downloads/en/details.aspx?displaylang=en&FamilyID=36a2630f-5d56-43b5-b996-7633f2ec14ff ii) copy qedit.h into c:\Program Files\Microsoft SDKs\Windows\vx.x\Include folder where x.x design the SDK version (last is 7.1) iii) compile with mex vcapg2.cpp -I"c:\Program Files\Microsoft SDKs\Windows\vx.x\Include" Thanks to Pr. Fehn for the x*** adaptation of vcapg2 Demos ----- 4 demos are included. i) "demo_mblbp" ii) "demo_chlbp" iii) "demo_haar" vi) "demo_detector_haar" Organization ------------ A) HAAR Features detector_haar Real-Time face detector based on Haar's features eval_haar Compute output of a trainned strong classifier for new instances eval_haar_subwindow Compute output of a trainned strong classifier for new instances at different scale fast_haar_ada_weaklearner Fast Decision stump weaklearner on Haar features fast_haar_adaboost_binary_model_cascade Train a strong classifier with Fastadaboosting on Haar features gui_features_dictionary GUI for creating patterns dictionary haar Compute the Haar features haar_ada_weaklearner Decision stump weaklearner for adaboosting on Haar features haar_adaboost_binary_model_cascade Train a strong classifier with Adaboosting on Haar features haar_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on Haar features haar_featlist Compute Haar features parameters haar_gentle_weaklearner Decision stump weaklearner for gentleboosting haar_gentleboost_binary_model_cascade Train a strong classifier with Gentleboosting on Haar features haar_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on Haar features Haar_matG Sparse Haar Feature matrix for fastadaboosting haar_scale Haar features scaled to Faces database size B) CHLBP features chlbp Compute the chlbp features chlbp_adaboost_binary_model_cascade Train a strong classifier with Adaboosting on chlbp features chlbp_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on chlbp features chlbp_gentleboost_binary_model_cascade Train a strong classifier with Gentleboosting on chlbp features chlbp_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on chlbp features eval_chlbp Compute output of a trainned strong classifier for new instances C) MBLBP features eval_mblbp Compute output of a trainned strong classifier for new instances mblbp Compute the mblbp features mblbp_ada_weaklearner Decision stump weaklearner for adaboosting on mblbp features mblbp_adaboost_binary_model_cascade Train a strong classifier with Adaboosting on mblbp features mblbp_adaboost_binary_predict_cascade Predict label with trained model with Adaboosting on mblbp features mblbp_featlist Compute mblbp features parameters mblbp_gentle_weaklearner Decision stump weaklearner for gentlboosting on mblbp features mblbp_gentleboost_binary_model_cascade Train a strong classifier with Gentleboosting on mblbp features mblbp_gentleboost_binary_predict_cascade Predict label with trained model with Gentleboosting on mblbp features detector_mblbp Real-Time face detector based on mblbp's features D) MISCELLANEOUS basicroc Compute ROC given true label and Outputs of Strong classifiers build_negatives Download from internet set of images used to construct negatives subwindows display_database Display all faces/non faces database generate_data Generate Positives & Negatives examples getmapping Mapping feature's values for CHLBP and MBLBP approaches (from Marko Heikkil and Timo Ahonen LBP toolbox) ieJPGSearch Included michaelB function to retrive URL links of images from Google image_integral_standard Standardize and compute Images Integral image_standard Standardize images imresize Resize images by bilinear interpolation int8tosparse Convert a int matrix in a sparse matrix (thx J. Tursa) inv_integral_image Retrieve images from images integral jensen_24x24 Ole Jensen faces/non faces database multi_exit_asymetric_boosting Train model with multi-exit asymetric boosting approach (see Pham Minh Tri works) nbfeat_haar Compute number of features given image database size and patterns plot_rectangle Display rectangles associated with detected faces rgb2gray Convert RGB image in gray format train_segment Train a segment model between two exit nodes vcapg2 Capture webcam frames (see Kazuyuki Kobayashi file from http://www.mathworks.com/matlabcentral/fileexchange/2939 ) viola_24x24 Viola-Jones faces/non faces database Author : Sébastien PARIS : sebastien.paris@lsis.org for contact and bugs reporting ------- Date : 02/20/2009 Main References : [1] R.E Schapire and al "Boosting the margin : A new explanation for the effectiveness of voting methods". ------------------ The annals of statistics, 1999 [2] Zhang, L. and Chu, R.F. and Xiang, S.M. and Liao, S.C. and Li, S.Z, "Face Detection Based on Multi-Block LBP Representation" ICB07 [3] C. Huang, H. Ai, Y. Li and S. Lao, "Learning sparse features in granular space for multi-view face detection", FG2006 [4] P.A Viola and M. Jones, "Robust real-time face detection", International Journal on Computer Vision, 2004 [5] M-T. Pham and all, "Detection with multi-exit asymetric boosting", CVPR'08 Greetings to : i ) Ole Jensen for providing me his faces database and the merging detections algorithm for detector_haar and detector_mblbp, ------------ ii) and also to Pham Minh Tri for his responses concerning Fastadaboosting and multi-exit asymetric boosting. Changelogs : ------------ - Fix some small bugs, better Linux*** support, include vcapg2 modified for win***. - Correct minor crashes - Should correctly compile with LCC and prior release of Matlab (R13 and upper versions) (thx to Bruno Luong for his function) - Should compile on Win*** platerform (thx to Soeren Sproessig for report bug) - Typo corrections

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