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Methods for the extraction of local features. Local features are descriptions of regions of images (SIFT, ...) selected by detectors (Difference of Gaussian, Harris, ...).
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/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   * 	Redistributions of source code must retain the above copyright notice,
 * 	this list of conditions and the following disclaimer.
 *
 *   *	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.
 *
 *   *	Neither the name of the University of Southampton 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 OWNER 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
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 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.feature.local.matcher;

import java.util.ArrayList;
import java.util.List;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.image.feature.local.keypoints.Keypoint;
import org.openimaj.knn.approximate.ByteNearestNeighboursKDTree;
import org.openimaj.util.pair.Pair;

/**
 * Basic keypoint matcher. Matches keypoints by finding closest Two keypoints to
 * target and checking whether the distance between the two matches is
 * sufficiently large.
 * 
 * This is the method for determining matches suggested by Lowe in the original
 * SIFT papers.
 * 
 * @author Jonathon Hare
 * @param 
 *            The type of keypoint
 */
@References(references = {
		@Reference(
				type = ReferenceType.Article,
				author = { "David Lowe" },
				title = "Distinctive image features from scale-invariant keypoints",
				year = "2004",
				journal = "IJCV",
				pages = { "91", "110" },
				month = "January",
				number = "2",
				volume = "60"),
		@Reference(
				type = ReferenceType.Inproceedings,
				author = { "David Lowe" },
				title = "Object recognition from local scale-invariant features",
				year = "1999",
				booktitle = "Proc. of the International Conference on Computer Vision {ICCV}",
				pages = { "1150", "1157" }
		)
})
public class FastBasicKeypointMatcher extends BasicMatcher {
	protected ByteNearestNeighboursKDTree modelKeypointsKNN;

	/**
	 * Construct with a threshold of 8, corresponding to the 0.8 in Lowe's IJCV
	 * paper
	 */
	public FastBasicKeypointMatcher()
	{
		super(8);
	}

	/**
	 * 
	 * @param threshold
	 *            threshold for determining matching keypoints
	 */
	public FastBasicKeypointMatcher(int threshold)
	{
		super(threshold);
	}

	/**
	 * Given a pair of images and their keypoints, pick the first keypoint from
	 * one image and find its closest match in the second set of keypoints. Then
	 * write the result to a file.
	 */
	@Override
	public boolean findMatches(List keys1)
	{
		matches = new ArrayList>();

		final byte[][] data = new byte[keys1.size()][];
		for (int i = 0; i < keys1.size(); i++)
			data[i] = keys1.get(i).ivec;

		final int[][] argmins = new int[keys1.size()][2];
		final float[][] mins = new float[keys1.size()][2];
		modelKeypointsKNN.searchKNN(data, 2, argmins, mins);

		for (int i = 0; i < keys1.size(); i++) {
			final float distsq1 = mins[i][0];
			final float distsq2 = mins[i][1];

			if (10 * 10 * distsq1 < thresh * thresh * distsq2) {
				matches.add(new Pair(keys1.get(i), modelKeypoints.get(argmins[i][0])));
			}
		}

		return true;
	}

	@Override
	public void setModelFeatures(List modelkeys) {
		modelKeypoints = modelkeys;

		final byte[][] data = new byte[modelkeys.size()][];
		for (int i = 0; i < modelkeys.size(); i++)
			data[i] = modelkeys.get(i).ivec;

		modelKeypointsKNN = new ByteNearestNeighboursKDTree(data, 1, 100);
	}
}