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textureThreshold
CvStereoBMState
track
CvFuzzyMeanShiftTracker
train
cv::FernClassifier::train()
cv::PlanarObjectDetector::train(const vector< Mat > &pyr, int _npoints=300, int _patchSize=FernClassifier::PATCH_SIZE, int _nstructs=FernClassifier::DEFAULT_STRUCTS, int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE, int _nviews=FernClassifier::DEFAULT_VIEWS, const LDetector &detector=LDetector(), const PatchGenerator &patchGenerator=PatchGenerator())
cv::PlanarObjectDetector::train(const vector< Mat > &pyr, const vector< KeyPoint > &keypoints, int _patchSize=FernClassifier::PATCH_SIZE, int _nstructs=FernClassifier::DEFAULT_STRUCTS, int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE, int _nviews=FernClassifier::DEFAULT_VIEWS, const LDetector &detector=LDetector(), const PatchGenerator &patchGenerator=PatchGenerator())
cv::RandomizedTree::train(std::vector< BaseKeypoint > const &base_set, RNG &rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
cv::RandomizedTree::train(std::vector< BaseKeypoint > const &base_set, RNG &rng, PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
cv::RTreeClassifier::train(std::vector< BaseKeypoint > const &base_set, RNG &rng, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS)
cv::RTreeClassifier::train(std::vector< BaseKeypoint > const &base_set, RNG &rng, PatchGenerator &make_patch, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS)
cv::DescriptorMatcher::train()
cv::FlannBasedMatcher::train()
cv::GenericDescriptorMatcher::train()
cv::OneWayDescriptorMatcher::train()
cv::FernDescriptorMatcher::train()
cv::VectorDescriptorMatcher::train()
CvNormalBayesClassifier::train(const CvMat *trainData, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, bool update=false)
CvNormalBayesClassifier::train(const cv::Mat &trainData, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), bool update=false)
CvKNearest::train(const CvMat *trainData, const CvMat *responses, const CvMat *sampleIdx=0, bool is_regression=false, int maxK=32, bool updateBase=false)
CvKNearest::train(const cv::Mat &trainData, const cv::Mat &responses, const cv::Mat &sampleIdx=cv::Mat(), bool isRegression=false, int maxK=32, bool updateBase=false)
CvSVM::train(const CvMat *trainData, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, CvSVMParams params=CvSVMParams())
CvSVM::train(const cv::Mat &trainData, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), CvSVMParams params=CvSVMParams())
CvEM::train(const CvMat *samples, const CvMat *sampleIdx=0, CvEMParams params=CvEMParams(), CvMat *labels=0)
CvEM::train(const cv::Mat &samples, const cv::Mat &sampleIdx=cv::Mat(), CvEMParams params=CvEMParams(), CV_OUT cv::Mat *labels=0)
CvDTree::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvDTreeParams params=CvDTreeParams())
CvDTree::train(CvMLData *trainData, CvDTreeParams params=CvDTreeParams())
CvDTree::train(CvDTreeTrainData *trainData, const CvMat *subsampleIdx)
CvDTree::train(const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvDTreeParams params=CvDTreeParams())
CvForestTree::train(CvDTreeTrainData *trainData, const CvMat *_subsample_idx, CvRTrees *forest)
CvForestTree::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvDTreeParams params=CvDTreeParams())
CvForestTree::train(CvDTreeTrainData *trainData, const CvMat *_subsample_idx)
CvRTrees::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvRTParams params=CvRTParams())
CvRTrees::train(CvMLData *data, CvRTParams params=CvRTParams())
CvRTrees::train(const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvRTParams params=CvRTParams())
CvERTrees::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvRTParams params=CvRTParams())
CvERTrees::train(const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvRTParams params=CvRTParams())
CvERTrees::train(CvMLData *data, CvRTParams params=CvRTParams())
CvBoostTree::train(CvDTreeTrainData *trainData, const CvMat *subsample_idx, CvBoost *ensemble)
CvBoostTree::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvDTreeParams params=CvDTreeParams())
CvBoostTree::train(CvDTreeTrainData *trainData, const CvMat *_subsample_idx)
CvBoost::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvBoostParams params=CvBoostParams(), bool update=false)
CvBoost::train(CvMLData *data, CvBoostParams params=CvBoostParams(), bool update=false)
CvBoost::train(const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvBoostParams params=CvBoostParams(), bool update=false)
CvGBTrees::train(const CvMat *trainData, int tflag, const CvMat *responses, const CvMat *varIdx=0, const CvMat *sampleIdx=0, const CvMat *varType=0, const CvMat *missingDataMask=0, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
CvGBTrees::train(CvMLData *data, CvGBTreesParams params=CvGBTreesParams(), bool update=false)
CvGBTrees::train(const cv::Mat &trainData, int tflag, const cv::Mat &responses, const cv::Mat &varIdx=cv::Mat(), const cv::Mat &sampleIdx=cv::Mat(), const cv::Mat &varType=cv::Mat(), const cv::Mat &missingDataMask=cv::Mat(), CvGBTreesParams params=CvGBTreesParams(), bool update=false)
CvANN_MLP::train(const CvMat *inputs, const CvMat *outputs, const CvMat *sampleWeights, const CvMat *sampleIdx=0, CvANN_MLP_TrainParams params=CvANN_MLP_TrainParams(), int flags=0)
CvANN_MLP::train(const cv::Mat &inputs, const cv::Mat &outputs, const cv::Mat &sampleWeights, const cv::Mat &sampleIdx=cv::Mat(), CvANN_MLP_TrainParams params=CvANN_MLP_TrainParams(), int flags=0)
train_auto
CvSVM::train_auto(const CvMat *trainData, const CvMat *responses, const CvMat *varIdx, const CvMat *sampleIdx, CvSVMParams params, int kfold=10, CvParamGrid Cgrid=get_default_grid(CvSVM::C), CvParamGrid gammaGrid=get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid=get_default_grid(CvSVM::P), CvParamGrid nuGrid=get_default_grid(CvSVM::NU), CvParamGrid coeffGrid=get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid=get_default_grid(CvSVM::DEGREE), bool balanced=false)
CvSVM::train_auto(const cv::Mat &trainData, const cv::Mat &responses, const cv::Mat &varIdx, const cv::Mat &sampleIdx, CvSVMParams params, int k_fold=10, CvParamGrid Cgrid=CvSVM::get_default_grid(CvSVM::C), CvParamGrid gammaGrid=CvSVM::get_default_grid(CvSVM::GAMMA), CvParamGrid pGrid=CvSVM::get_default_grid(CvSVM::P), CvParamGrid nuGrid=CvSVM::get_default_grid(CvSVM::NU), CvParamGrid coeffGrid=CvSVM::get_default_grid(CvSVM::COEF), CvParamGrid degreeGrid=CvSVM::get_default_grid(CvSVM::DEGREE), bool balanced=false)
trainPath
cv::OneWayDescriptorMatcher::Params
tsNone
CvFuzzyMeanShiftTracker
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