//fix phase of eigensystem and store phase of first entry of each eigenvector void fix_phase(Eigen::MatrixXcd& V, Eigen::MatrixXcd& V_fix, std::vector<double>& phase) { const int V3 = pars -> get_int("V3"); //helper variables: //Number of eigenvectors int n_ev; //negative imaginary std::complex<double> i_neg (0,-1); //tmp factor and phase std::complex<double> fac (1.,1.); double tmp_phase = 0; //get sizes right, resize if necessary n_ev = V.cols(); if (phase.size() != n_ev) phase.resize(n_ev); if (V_fix.cols() != n_ev) V_fix.resize(3*V3,n_ev); //loop over all eigenvectors of system for (int n = 0; n < n_ev; ++n) { tmp_phase = std::arg(V(0,n)); phase.at(n) = tmp_phase; fac = std::exp(i_neg*tmp_phase); //Fix phase of eigenvector with negative polar angle of first entry V_fix.col(n) = fac * V.col(n); } }
void CCorrelationFilters::build_mmcf(struct CDataStruct *img, struct CParamStruct *params, struct CFilterStruct *filt) { /* * This function calls the correlation filter design proposed in the following publications. * * A. Rodriguez, Vishnu Naresh Boddeti, B.V.K. Vijaya Kumar and A. Mahalanobis, "Maximum Margin Correlation Filter: A New Approach for Localization and Classification", IEEE Transactions on Image Processing, 2012. * * Vishnu Naresh Boddeti, "Advances in Correlation Filters: Vector Features, Structured Prediction and Shape Alignment" PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2012. * * Vishnu Naresh Boddeti and B.V.K. Vijaya Kumar, "Maximum Margin Vector Correlation Filters," Arxiv 1404.6031 (April 2014). * * Notes: This currently the best performing Correlation Filter design, especially when the training sample size is larger than the dimensionality of the data. */ filt->params = *params; filt->filter.size_data = params->size_filt_freq; filt->filter.size_data_freq = params->size_filt_freq; filt->filter.num_elements_freq = img->num_elements_freq; params->num_elements_freq = img->num_elements_freq; filt->filter.data_freq = new complex<double>[img->num_elements_freq*img->num_channels]; Eigen::ArrayXcd filt_freq = Eigen::ArrayXcd::Zero(params->num_elements_freq*img->num_channels); // If not set default to 1 if (params->wpos < 1) params->wpos = 1; filt->params.wpos = params->wpos; compute_psd_matrix(img, params); Eigen::MatrixXcd Y = Eigen::MatrixXcd::Zero(img->num_elements_freq*img->num_channels,img->num_data); Eigen::MatrixXcd u = Eigen::MatrixXcd::Zero(img->num_data,1); Eigen::MatrixXd temp = Eigen::MatrixXd::Zero(img->num_data,img->num_data); Eigen::Map<Eigen::MatrixXcd> X(img->data_freq,img->num_elements_freq*img->num_channels,img->num_data); Eigen::ArrayXXcd temp1 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::ArrayXXcd temp2 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::Vector2i num_blocks_1, num_blocks_2; num_blocks_1 << img->num_channels,img->num_channels; num_blocks_2 << img->num_channels,1; for (int k=0;k<img->num_data;k++){ temp2 = X.block(0,k,img->num_elements_freq*img->num_channels,1).array(); temp2.resize(img->num_elements_freq,img->num_channels); fusion_matrix_multiply(temp1, img->Sinv, temp2, num_blocks_1, num_blocks_2); temp1.resize(img->num_elements_freq*img->num_channels,1); Y.block(0,k,img->num_elements_freq*img->num_channels,1) = temp1.matrix(); temp1.resize(img->num_elements_freq,img->num_channels); if (img->labels[k] == 1) { u(k) = std::complex<double>(params->wpos,0); } else { u(k) = std::complex<double>(-1,0); } } esvm::SVMClassifier libsvm; libsvm.setC(params->C); libsvm.setKernel(params->kernel_type); libsvm.setWpos(params->wpos); temp = (X.conjugate().transpose()*Y).real(); Eigen::Map<Eigen::MatrixXd> y(img->labels,img->num_data,1); libsvm.train(temp, y); temp.resize(0,0); int nSV; libsvm.getNSV(&nSV); Eigen::VectorXi sv_indices = Eigen::VectorXi::Zero(nSV); Eigen::VectorXd sv_coef = Eigen::VectorXd::Zero(nSV); libsvm.getSI(sv_indices); libsvm.getCoeff(sv_coef); for (int k=0; k<nSV; k++) { filt_freq += (Y.block(0,sv_indices[k]-1,img->num_elements_freq*img->num_channels,1)*sv_coef[k]).array(); } Y.resize(0,0); Eigen::Map<Eigen::ArrayXcd>(filt->filter.data_freq,img->num_elements_freq*img->num_channels) = filt_freq; filt->filter.num_data = 1; filt->filter.num_channels = img->num_channels; filt->filter.ptr_data.reserve(filt->filter.num_data); filt->filter.ptr_data_freq.reserve(filt->filter.num_data); ifft_data(&filt->filter); fft_data(&filt->filter); }
void CCorrelationFilters::build_otsdf(struct CDataStruct *img, struct CParamStruct *params, struct CFilterStruct *filt) { /* * This function implements the correlation filter design proposed in the following publications. * * [1] Optimal trade-off synthetic discriminant function filters for arbitrary devices, B.V.K.Kumar, D.W.Carlson, A.Mahalanobis - Optics Letters, 1994. * * [2] Jason Thornton, "Matching deformed and occluded iris patterns: a probabilistic model based on discriminative cues." PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2007. * * [3] Vishnu Naresh Boddeti, Jonathon M Smereka, and B. V. K. Vijaya Kumar, "A comparative evaluation of iris and ocular recognition methods on challenging ocular images." IJCB, 2011. * * [4] A. Mahalanobis, B.V.K. Kumar, D. Casasent, "Minimum average correlation energy filters," Applied Optics, 1987 * * Notes: This filter design is good when the dimensionality of the data is greater than the training sample size. Setting the filter parameter params->alpha=0 results in the popular MACE filter. However, it is usually better to set alpha to a small number rather than setting it to 0. If you use MACE cite [4]. */ filt->params = *params; filt->filter.size_data = params->size_filt_freq; filt->filter.size_data_freq = params->size_filt_freq; filt->filter.num_elements_freq = img->num_elements_freq; params->num_elements_freq = img->num_elements_freq; filt->filter.data_freq = new complex<double>[img->num_elements_freq*img->num_channels]; Eigen::ArrayXcd filt_freq = Eigen::ArrayXcd::Zero(params->num_elements_freq*img->num_channels); // If not set default to 1 if (params->wpos < 1) params->wpos = 1; filt->params.wpos = params->wpos; compute_psd_matrix(img, params); Eigen::MatrixXcd Y = Eigen::MatrixXcd::Zero(img->num_elements_freq*img->num_channels,img->num_data); Eigen::MatrixXcd u = Eigen::MatrixXcd::Zero(img->num_data,1); Eigen::MatrixXcd temp = Eigen::MatrixXcd::Zero(img->num_data,img->num_data); Eigen::MatrixXd tmp = Eigen::MatrixXd::Zero(img->num_data,img->num_data); Eigen::Map<Eigen::MatrixXcd> X(img->data_freq,img->num_elements_freq*img->num_channels,img->num_data); Eigen::ArrayXXcd temp1 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::ArrayXXcd temp2 = Eigen::ArrayXXcd::Zero(img->num_elements_freq,img->num_channels); Eigen::Vector2i num_blocks_1, num_blocks_2; num_blocks_1 << img->num_channels,img->num_channels; num_blocks_2 << img->num_channels,1; for (int k=0;k<img->num_data;k++){ temp2 = X.block(0,k,img->num_elements_freq*img->num_channels,1).array(); temp2.resize(img->num_elements_freq,img->num_channels); fusion_matrix_multiply(temp1, img->Sinv, temp2, num_blocks_1, num_blocks_2); temp1.resize(img->num_elements_freq*img->num_channels,1); Y.block(0,k,img->num_elements_freq*img->num_channels,1) = temp1.matrix(); temp1.resize(img->num_elements_freq,img->num_channels); if (img->labels[k] == 1) { u(k) = std::complex<double>(params->wpos,0); } else { u(k) = std::complex<double>(1,0); } } temp = X.conjugate().transpose()*Y; temp = temp.ldlt().solve(u); filt_freq = Y*temp; Y.resize(0,0); Eigen::Map<Eigen::ArrayXcd>(filt->filter.data_freq,img->num_elements_freq*img->num_channels) = filt_freq; filt->filter.num_data = 1; filt->filter.num_channels = img->num_channels; filt->filter.ptr_data.reserve(filt->filter.num_data); filt->filter.ptr_data_freq.reserve(filt->filter.num_data); ifft_data(&filt->filter); fft_data(&filt->filter); }