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27 Mar 2013 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

hi,Sebastien,when I run the train_cascade.m, I set the specific parameters as follows: options.algoboost = 2,The initial number of negative samples are 2500,The initial number of postives samples are 2500,options.rect_param =[ 19 types of features];
options.maxstage = 9;When the code run the stage=6,the results as follow:generate_data_cascade2 6 in 5132.308 s
Train weaklearner 1 in 2.969 s
stage 6/9, m = 1, alpham = 1.0000
stage 6/9, m = 1, betam = 0.0000
Train weaklearner 2 in 3.074 s
stage 6/9, m = 2, alpham = 1.0000
stage 6/9, m = 2, betam = 0.0000
Train weaklearner 3 in 3.074 s
stage 6/9, m = 3, alpham = 1.0000
stage 6/9, m = 3, betam = 0.0000
Train weaklearner 4 in 3.130 s
stage 6/9, m = 4, alpham = 1.0000
stage 6/9, m = 4, betam = 0.0000
Train weaklearner 5 in 3.088 s
stage 6/9, m = 5, alpham = 0.9010
stage 6/9, m = 5, betam = 0.0020
Train weaklearner 6 in 3.094 s
stage 6/9, m = 6, alpham = 0.7920
stage 6/9, m = 6, betam = 0.0080
Train weaklearner 7 in 3.127 s
stage 6/9, m = 7, alpham = 0.6550
stage 6/9, m = 7, betam = 0.0090
Train weaklearner 8 in 3.116 s
stage 6/9, m = 8, alpham = 0.7340
stage 6/9, m = 8, betam = 0.0090
Train weaklearner 9 in 3.113 s
stage 6/9, m = 9, alpham = 0.6600
stage 6/9, m = 9, betam = 0.0080
Train weaklearner 10 in 3.100 s
stage 6/9, m = 10, alpham = 0.6530
stage 6/9, m = 10, betam = 0.0090
Train weaklearner 11 in 3.140 s
stage 6/9, m = 11, alpham = 0.7430
stage 6/9, m = 11, betam = 0.0090
Train weaklearner 12 in 3.191 s
stage 6/9, m = 12, alpham = 0.7500
stage 6/9, m = 12, betam = 0.0090
Train weaklearner 13 in 3.049 s
stage 6/9, m = 13, alpham = 0.6300
stage 6/9, m = 13, betam = 0.0090
Train weaklearner 14 in 3.194 s
stage 6/9, m = 14, alpham = 0.5940
stage 6/9, m = 14, betam = 0.0090
Train weaklearner 15 in 3.241 s
stage 6/9, m = 15, alpham = 0.5550
stage 6/9, m = 15, betam = 0.0090
Train weaklearner 16 in 3.129 s
stage 6/9, m = 16, alpham = 0.6050
stage 6/9, m = 16, betam = 0.0090
Train weaklearner 17 in 3.128 s
stage 6/9, m = 17, alpham = 0.5730
stage 6/9, m = 17, betam = 0.0090
Train weaklearner 18 in 3.154 s
stage 6/9, m = 18, alpham = 0.5690
stage 6/9, m = 18, betam = 0.0090
Train weaklearner 19 in 3.104 s
stage 6/9, m = 19, alpham = 0.6060
stage 6/9, m = 19, betam = 0.0090
Train weaklearner 20 in 3.251 s
??? Attempted to access
options.betaperstage(6); index out of
bounds because
numel(options.betaperstage)=5.

Error in ==> train_cascade at 547
options.stat(: , nb_stage) =
[stat(:) ;
options.betaperstage(nb_stage) ;
options.alphaperstage(nb_stage)];
Have you met such problem some times ago?How can I improve it? Thanks.

27 Mar 2013 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

23 Feb 2013 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

Hi,Sébastien,I find the results are different when I debug the train_cascade.m in liunx(64bits) and in xp(32bits). The code is same . Can you tell me why? Thanks.

02 Oct 2012 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

Dear Sir,
When I debug the omp.c,the result is :omp.c(10) : fatal error C1021: Invalid preprocessor command“#warning” .I try to modify it ,but the result also can not pass,please help me,thanks!

15 Sep 2012 Objects/Faces Detection Toolbox Objects/Faces detection using Local Binary Patterns and Haar features Author: Sebastien PARIS

dear Sebastien
I want to download your new updated Objects/Faces detection toolbox and some references ,but I can not find the address,please send them to my email :
qiguangjing123@126.com,thank you very much.

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