/*
* This file is part of the LIRE project: http://lire-project.net
* LIRE is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* LIRE is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LIRE; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* We kindly ask you to refer the any or one of the following publications in
* any publication mentioning or employing Lire:
*
* Lux Mathias, Savvas A. Chatzichristofis. Lire: Lucene Image Retrieval –
* An Extensible Java CBIR Library. In proceedings of the 16th ACM International
* Conference on Multimedia, pp. 1085-1088, Vancouver, Canada, 2008
* URL: http://doi.acm.org/10.1145/1459359.1459577
*
* Lux Mathias. Content Based Image Retrieval with LIRE. In proceedings of the
* 19th ACM International Conference on Multimedia, pp. 735-738, Scottsdale,
* Arizona, USA, 2011
* URL: http://dl.acm.org/citation.cfm?id=2072432
*
* Mathias Lux, Oge Marques. Visual Information Retrieval using Java and LIRE
* Morgan & Claypool, 2013
* URL: http://www.morganclaypool.com/doi/abs/10.2200/S00468ED1V01Y201301ICR025
*
* Copyright statement:
* --------------------
* (c) 2002-2013 by Mathias Lux (mathias@juggle.at)
* http://www.semanticmetadata.net/lire, http://www.lire-project.net
*/
package net.semanticmetadata.lire.imageanalysis.features.local.sift;
import java.awt.geom.AffineTransform;
import java.util.Collection;
import java.util.Random;
/**
* Abstract class for arbitrary geometric transformation models to be applied
* to points in n-dimensional space.
* <p/>
* Provides methods for generic optimization and model extraction algorithms.
* Currently, RANSAC and Monte-Carlo minimization implemented. Needs revision...
* <p/>
* TODO A model is planned to be a generic transformation pipeline to be
* applied to images, volumes or arbitrary sets of n-dimensional points. E.g.
* lens transformation of camera images, pose and location of mosaic tiles,
* non-rigid bending of confocal stacks etc.
* <p/>
* License: GPL
* <p/>
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License 2
* as published by the Free Software Foundation.
* <p/>
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
* <p/>
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
* <p/>
* NOTE:
* The SIFT-method is protected by U.S. Patent 6,711,293: "Method and
* apparatus for identifying scale invariant features in an image and use of
* same for locating an object in an image" by the University of British
* Columbia. That is, for commercial applications the permission of the author
* is required.
*
* @author Stephan Saalfeld <saalfeld@mpi-cbg.de>
* @version 0.1b
*/
abstract public class Model {
// minimal number of point correspondences required to solve the model
static final public int MIN_SET_SIZE = 0;
// real random
//final Random random = new Random( System.currentTimeMillis() );
// repeatable results
final static Random rnd = new Random(69997);
/**
* error depends on what kind of algorithm is running
* small error is better than large error
*/
public double error;
/**
* instantiates an empty model with maximally large error
*/
public Model() {
error = Double.MAX_VALUE;
}
/**
* fit the model to a minimal set of point correpondences
* estimates a model to transform match.p2.local to match.p1.world
*
* @param min_matches minimal set of point correpondences
* @return true if a model was estimated
*/
public abstract boolean fit(PointMatch[] min_matches);
/**
* apply the model to a point location
*
* @param point
* @return transformed point
*/
public abstract float[] apply(float[] point);
/**
* apply the model to a point location
*
* @param point
*/
public abstract void applyInPlace(float[] point);
/**
* apply the inverse of the model to a point location
*
* @param point
* @return transformed point
*/
public abstract float[] applyInverse(float[] point);
/**
* apply the inverse of the model to a point location
*
* @param point
*/
public abstract void applyInverseInPlace(float[] point);
/**
* test the model for a set of point correspondence candidates
* <p/>
* clears inliers and fills it with the fitting subset of candidates
*
* @param candidates set of point correspondence candidates
* @param inliers set of point correspondences that fit the model
* @param epsilon maximal allowed transfer error
* @param min_inliers minimal ratio of inliers (0.0 => 0%, 1.0 => 100%)
*/
public boolean test(
Collection<PointMatch> candidates,
Collection<PointMatch> inliers,
double epsilon,
double min_inlier_ratio) {
inliers.clear();
for (PointMatch m : candidates) {
m.apply(this);
if (m.getDistance() < epsilon) inliers.add(m);
}
float ir = (float) inliers.size() / (float) candidates.size();
error = 1.0 - ir;
if (error > 1.0)
error = 1.0;
if (error < 0)
error = 0.0;
return (ir > min_inlier_ratio);
}
/**
* less than operater to make the models comparable, returns false for error < 0
*
* @param m
* @return false for error < 0, otherwise true if this.error is smaller than m.error
*/
public boolean betterThan(Model m) {
if (error < 0) return false;
return error < m.error;
}
/**
* randomly change the model a bit
* <p/>
* estimates the necessary amount of shaking for each single dimensional
* distance in the set of matches
*
* @param matches point matches
* @param scale gives a multiplicative factor to each dimensional distance (scales the amount of shaking)
* @param center local pivot point for centered shakes (e.g. rotation)
*/
abstract public void shake(
Collection<PointMatch> matches,
float scale,
float[] center);
abstract public void minimize(Collection<PointMatch> matches);
abstract public AffineTransform getAffine();
/**
* string to output stream
*/
abstract public String toString();
/**
* clone
*/
abstract public Model clone();
};