org.openimaj.image.feature.local.engine.DoGColourSIFTEngine Maven / Gradle / Ivy
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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
* 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.
*/
package org.openimaj.image.feature.local.engine;
import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.feature.local.list.LocalFeatureList;
import org.openimaj.image.FImage;
import org.openimaj.image.MBFImage;
import org.openimaj.image.analysis.pyramid.gaussian.GaussianOctave;
import org.openimaj.image.analysis.pyramid.gaussian.GaussianPyramid;
import org.openimaj.image.colour.ColourSpace;
import org.openimaj.image.feature.local.descriptor.gradient.SIFTFeatureProvider;
import org.openimaj.image.feature.local.detector.dog.collector.Collector;
import org.openimaj.image.feature.local.detector.dog.collector.OctaveKeypointCollector;
import org.openimaj.image.feature.local.detector.dog.extractor.ColourGradientFeatureExtractor;
import org.openimaj.image.feature.local.detector.dog.extractor.DominantOrientationExtractor;
import org.openimaj.image.feature.local.detector.dog.extractor.OrientationHistogramExtractor;
import org.openimaj.image.feature.local.detector.dog.pyramid.FirstBandDoGOctaveExtremaFinder;
import org.openimaj.image.feature.local.detector.pyramid.BasicOctaveExtremaFinder;
import org.openimaj.image.feature.local.detector.pyramid.OctaveInterestPointFinder;
import org.openimaj.image.feature.local.keypoints.Keypoint;
/**
* The {@link DoGSIFTEngine} extended to colour images (aka Colour-SIFT).
*
* The {@link DoGColourSIFTEngine} creates a luminance image from which to apply
* the difference-of-Gaussian interest point detection algorithm, but extracts
* the actual SIFT features from the bands of the input image directly. This
* means that the type of Colour-SIFT feature is controlled directly by the
* colour-space of the input image; for example if an RGB image is given as
* input, then the feature will be standard RGB-SIFT.
*
* @author Jonathon Hare ([email protected])
*/
@References(references = {
@Reference(
type = ReferenceType.Article,
author = { "Burghouts, Gertjan J.", "Geusebroek, Jan-Mark" },
title = "Performance evaluation of local colour invariants",
year = "2009",
journal = "Comput. Vis. Image Underst.",
pages = { "48", "", "62" },
url = "http://dx.doi.org/10.1016/j.cviu.2008.07.003",
month = "jan",
number = "1",
publisher = "Elsevier Science Inc.",
volume = "113",
customData = {
"issn", "1077-3142",
"numpages", "15",
"doi", "10.1016/j.cviu.2008.07.003",
"acmid", "1465842",
"address", "New York, NY, USA",
"keywords", "Colour, Local descriptors, SIFT"
}
),
@Reference(
type = ReferenceType.Article,
author = { "van de Sande, K. E. A.", "Gevers, T.", "Snoek, C. G. M." },
title = "Evaluating Color Descriptors for Object and Scene Recognition",
year = "2010",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
pages = { "1582", "", "1596" },
url = "http://www.science.uva.nl/research/publications/2010/vandeSandeTPAMI2010",
number = "9",
volume = "32"
)
})
public class DoGColourSIFTEngine implements Engine {
DoGSIFTEngineOptions options;
/**
* Construct with the default values for the {@link DoGSIFTEngineOptions}.
*/
public DoGColourSIFTEngine() {
this(new DoGSIFTEngineOptions());
}
/**
* Construct with the given options.
*
* @param options
* the options.
*/
public DoGColourSIFTEngine(DoGSIFTEngineOptions options) {
this.options = options;
}
@Override
public LocalFeatureList findFeatures(MBFImage image) {
final FImage luminance = ColourSpace.convert(image, ColourSpace.LUMINANCE_NTSC).bands.get(0);
return findFeatures(image, luminance);
}
/**
* Find DoG interest points in the given luminance image, but extract the
* SIFT features from the colour image.
*
* @param image
* the colour image to extract the SIFT features from
* @param luminance
* the luminance image to detect the interest points in
* @return the extracted features
*/
public LocalFeatureList findFeatures(MBFImage image, FImage luminance) {
final MBFImage newimage = new MBFImage(ColourSpace.CUSTOM);
newimage.bands.add(luminance);
newimage.bands.addAll(image.bands);
return findFeaturesInternal(newimage);
}
protected LocalFeatureList findFeaturesInternal(MBFImage image) {
final OctaveInterestPointFinder, MBFImage> finder =
new FirstBandDoGOctaveExtremaFinder(new BasicOctaveExtremaFinder(options.magnitudeThreshold,
options.eigenvalueRatio));
final Collector, Keypoint, MBFImage> collector = new OctaveKeypointCollector(
new ColourGradientFeatureExtractor(
new DominantOrientationExtractor(
options.peakThreshold,
new OrientationHistogramExtractor(
options.numOriHistBins,
options.scaling,
options.smoothingIterations,
options.samplingSize
)
),
new SIFTFeatureProvider(
options.numOriBins,
options.numSpatialBins,
options.valueThreshold,
options.gaussianSigma
),
options.magnificationFactor * options.numSpatialBins
)
);
finder.setOctaveInterestPointListener(collector);
options.setOctaveProcessor(finder);
final GaussianPyramid pyr = new GaussianPyramid(options);
pyr.process(image);
return collector.getFeatures();
}
/**
* @return the current options used by the engine
*/
public DoGSIFTEngineOptions getOptions() {
return options;
}
}