diff --git a/modules/img/alg/src/CMakeLists.txt b/modules/img/alg/src/CMakeLists.txt
index 5bdb78b01aaa73385130901273ef53a40db17878..5df3fd55852d08aad58d14147fdf1b7d7c9ec0da 100644
--- a/modules/img/alg/src/CMakeLists.txt
+++ b/modules/img/alg/src/CMakeLists.txt
@@ -59,7 +59,6 @@ gaussian.hh
gaussian_laplacian.hh
highest_peak_search_3d.hh
histogram.hh
-levenberg_marquardt.h
line_iterator.hh
local_sigma_threshold.hh
mask_image.hh
diff --git a/modules/img/alg/src/levenberg_marquardt.h b/modules/img/alg/src/levenberg_marquardt.h
deleted file mode 100644
index 52773bc6a5e93c820695ee9f30967e3b0e2157a2..0000000000000000000000000000000000000000
--- a/modules/img/alg/src/levenberg_marquardt.h
+++ /dev/null
@@ -1,177 +0,0 @@
-// Copyright (c) 2007, 2008, 2009 libmv authors.
-//
-// Permission is hereby granted, free of charge, to any person obtaining a copy
-// of this software and associated documentation files (the "Software"), to
-// deal in the Software without restriction, including without limitation the
-// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
-// sell copies of the Software, and to permit persons to whom the Software is
-// furnished to do so, subject to the following conditions:
-//
-// The above copyright notice and this permission notice shall be included in
-// all copies or substantial portions of the Software.
-//
-// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
-// IN THE SOFTWARE.
-//
-// A simple implementation of levenberg marquardt.
-//
-// [1] K. Madsen, H. Nielsen, O. Tingleoff. Methods for Non-linear Least
-// Squares Problems.
-// http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
-//
-// TODO(keir): Cite the Lourakis' dogleg paper.
-
-#ifndef LIBMV_NUMERIC_LEVENBERG_MARQUARDT_H
-#define LIBMV_NUMERIC_LEVENBERG_MARQUARDT_H
-
-#include <cmath>
-#include <Eigen/Core>
-#include <Eigen/Eigenvalues>
-
-
-namespace ost { namespace img { namespace alg {
-
-template<typename Function,
- typename Jacobian,
- typename Solver = Eigen::JacobiSVD<
- Eigen::Matrix<typename Function::FMatrixType::RealScalar,
- Function::XMatrixType::RowsAtCompileTime,
- Function::XMatrixType::RowsAtCompileTime> > >
-class LevenbergMarquardt {
- public:
- typedef typename Function::XMatrixType::RealScalar Scalar;
- typedef typename Function::FMatrixType FVec;
- typedef typename Function::XMatrixType Parameters;
- typedef Eigen::Matrix<typename Function::FMatrixType::RealScalar,
- Function::FMatrixType::RowsAtCompileTime,
- Function::XMatrixType::RowsAtCompileTime> JMatrixType;
- typedef Eigen::Matrix<typename JMatrixType::RealScalar,
- JMatrixType::ColsAtCompileTime,
- JMatrixType::ColsAtCompileTime> AMatrixType;
-
- // TODO(keir): Some of these knobs can be derived from each other and
- // removed, instead of requiring the user to set them.
- enum Status {
- RUNNING,
- GRADIENT_TOO_SMALL, // eps > max(J'*f(x))
- RELATIVE_STEP_SIZE_TOO_SMALL, // eps > ||dx|| / ||x||
- ERROR_TOO_SMALL, // eps > ||f(x)||
- HIT_MAX_ITERATIONS,
- };
-
- LevenbergMarquardt(const Function &f)
- : f_(f), df_(f) {}
-
- struct SolverParameters {
- SolverParameters()
- : gradient_threshold(1e-20),
- relative_step_threshold(1e-20),
- error_threshold(1e-16),
- initial_scale_factor(1e-3),
- max_iterations(200) {}
- Scalar gradient_threshold; // eps > max(J'*f(x))
- Scalar relative_step_threshold; // eps > ||dx|| / ||x||
- Scalar error_threshold; // eps > ||f(x)||
- Scalar initial_scale_factor; // Initial u for solving normal equations.
- int max_iterations; // Maximum number of solver iterations.
- };
-
- struct Results {
- Scalar error_magnitude; // ||f(x)||
- Scalar gradient_magnitude; // ||J'f(x)||
- int iterations;
- Status status;
- };
-
- Status Update(const Parameters &x, const SolverParameters ¶ms,
- JMatrixType *J, AMatrixType *A, FVec *error, Parameters *g) {
- *J = df_(x);
- *A = (*J).transpose() * (*J);
- *error = -f_(x);
- *g = (*J).transpose() * *error;
- if (g->array().abs().maxCoeff() < params.gradient_threshold) {
- return GRADIENT_TOO_SMALL;
- } else if (error->norm() < params.error_threshold) {
- return ERROR_TOO_SMALL;
- }
- return RUNNING;
- }
-
- Results minimize(Parameters *x_and_min) {
- SolverParameters params;
- return minimize(params, x_and_min);
- }
-
- Results minimize(const SolverParameters ¶ms, Parameters *x_and_min) {
- Parameters &x = *x_and_min;
- JMatrixType J;
- AMatrixType A;
- FVec error;
- Parameters g;
-
- Results results;
- results.status = Update(x, params, &J, &A, &error, &g);
-
- Scalar u = Scalar(params.initial_scale_factor*A.diagonal().maxCoeff());
- Scalar v = 2;
-
- Parameters dx, x_new;
- int i;
- for (i = 0; results.status == RUNNING && i < params.max_iterations; ++i) {
-// LOG(INFO) << "iteration: " << i;
-// LOG(INFO) << "||f(x)||: " << f_(x).norm();
-// LOG(INFO) << "max(g): " << g.array().abs().maxCoeff();
-// LOG(INFO) << "u: " << u;
-// LOG(INFO) << "v: " << v;
- AMatrixType A_augmented = A + u*AMatrixType::Identity(J.cols(), J.cols());
- Solver solver(A_augmented, Eigen::ComputeThinU | Eigen::ComputeThinV);
- dx = solver.solve(g);
- if (dx.norm() <= params.relative_step_threshold * x.norm()) {
- results.status = RELATIVE_STEP_SIZE_TOO_SMALL;
- break;
- }
-
- x_new = x + dx;
- // Rho is the ratio of the actual reduction in error to the reduction
- // in error that would be obtained if the problem was linear.
- // See [1] for details.
- Scalar rho((error.squaredNorm() - f_(x_new).squaredNorm())
- / dx.dot(u*dx + g));
- if (rho > 0) {
- // Accept the Gauss-Newton step because the linear model fits well.
- x = x_new;
- results.status = Update(x, params, &J, &A, &error, &g);
- Scalar tmp = Scalar(2*rho-1);
- u = u*std::max(Scalar(1/3.), 1 - (tmp*tmp*tmp));
- v = 2;
- continue;
- }
-
- // Reject the update because either the normal equations failed to solve
- // or the local linear model was not good (rho < 0). Instead, increase u
- // to move closer to gradient descent.
- u *= v;
- v *= 2;
- }
- if (results.status == RUNNING) {
- results.status = HIT_MAX_ITERATIONS;
- }
- results.error_magnitude = error.norm();
- results.gradient_magnitude = g.norm();
- results.iterations = i;
- return results;
- }
-
- private:
- const Function &f_;
- Jacobian df_;
-};
-
-}}} // namespace mv
-
-#endif // LIBMV_NUMERIC_LEVENBERG_MARQUARDT_H