iPiano
An implementation of the iPiano algorithms for non-convex and non-smooth optimization.
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Classes
Functionals Class Reference

A collection of functionals for different computer vision/image processing tasks used to demonstrate the use of nmiPiano/iPiano optimization. More...

#include <functionals.h>

Classes

class  CompressiveSensing
 Compressive sensing example using convex optimization as discussed in [1]: [1] G. Kutyniok. Compressed Sensing: Theory and Applications. Computing Research Repository, abs/1203.3815, 2012. More...
 
class  Denoising
 Denoising functionals (for grayscale images!. More...
 
class  PhaseField
 Two Phase-Field for segmentation, see [1]. [1] Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts of Intelligent Vision Systems, 2005. More...
 
class  PhaseField_Color
 Two Phase-Field for color image segmentation, see [1]; adapted to color. [1] Shen. Gamma-Convergence Approximation to Piecewise Constant Mumford-Shah Segmentation. International Conference on Advanced Concepts of Intelligent Vision Systems, 2005. More...
 
class  Util
 Utilities for using nmiPiano/iPiano for computer vision/image processing. More...
 

Detailed Description

A collection of functionals for different computer vision/image processing tasks used to demonstrate the use of nmiPiano/iPiano optimization.

Implementation of several utilities and functions for applying iPiano and nmiPiano, as described in [1] and [2], to computer vision problems:

[1] P. Ochs, Y. Chen, T. Brox, T. Pock. iPiano: Inertial Proximal Algorithm for Nonconvex Optimization SIAM Journal of Imaging Sciences, colume 7, number 2, 2014. [2] D. Stutz. Seminar paper "iPiano: Inertial Proximal Algorithm for Non-Convex Optimization" https://github.com/davidstutz/seminar-ipiano

The code is published under the BSD 3-Clause:

Copyright (c) 2016, David Stutz All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
  3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Author
David Stutz

The documentation for this class was generated from the following file: