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Methods for the extraction of low-level image features, including global image features and pixel/patch classification models.

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/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
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 * 	contributors may be used to endorse or promote products derived from this
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package org.openimaj.image.feature.dense.gradient;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.image.FImage;
import org.openimaj.image.analyser.ImageAnalyser;
import org.openimaj.image.analysis.algorithm.histogram.GradientOrientationHistogramExtractor;
import org.openimaj.image.analysis.algorithm.histogram.binning.SpatialBinningStrategy;
import org.openimaj.image.feature.dense.gradient.binning.FixedHOGStrategy;
import org.openimaj.image.feature.dense.gradient.binning.FlexibleHOGStrategy;
import org.openimaj.image.processing.convolution.FImageGradients;
import org.openimaj.math.geometry.shape.Rectangle;
import org.openimaj.math.statistics.distribution.Histogram;

/**
 * Implementation of an extractor for the Histogram of Oriented Gradients (HOG)
 * feature for object detection. This implementation allows any kind of spatial
 * layout to be used through different implementations of
 * {@link SpatialBinningStrategy}s. HOG features can be efficiently extracted
 * for many windows of the image.
 * 

* The actual work of computing and normalising the descriptor is performed by * the {@link SpatialBinningStrategy} (i.e. a {@link FixedHOGStrategy} or * {@link FlexibleHOGStrategy}); this class just provides the objects required * for efficient histogram computation (namely a * {@link GradientOrientationHistogramExtractor}) for the image being analysed. *

* Normally, HOG features are computed using all gradients in the image, but * this class makes it possible to only consider gradients along "edges" using * the {@link #analyseImage(FImage, FImage)} method. * * @author Jonathon Hare ([email protected]) */ @Reference( type = ReferenceType.Inproceedings, author = { "Dalal, Navneet", "Triggs, Bill" }, title = "Histograms of Oriented Gradients for Human Detection", year = "2005", booktitle = "Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01", pages = { "886", "", "893" }, url = "http://dx.doi.org/10.1109/CVPR.2005.177", publisher = "IEEE Computer Society", series = "CVPR '05", customData = { "isbn", "0-7695-2372-2", "numpages", "8", "doi", "10.1109/CVPR.2005.177", "acmid", "1069007", "address", "Washington, DC, USA" }) public class HOG implements ImageAnalyser { GradientOrientationHistogramExtractor extractor; protected SpatialBinningStrategy strategy; private transient Histogram currentHist; /** * Construct a new {@link HOG} with the 9 bins, using histogram * interpolation and unsigned gradients. Use the given strategy to extract * the actual features. * * @param strategy * the {@link SpatialBinningStrategy} to use to produce the * features */ public HOG(SpatialBinningStrategy strategy) { this(9, true, FImageGradients.Mode.Unsigned, strategy); } /** * Construct a new {@link HOG} with the given number of bins. Optionally * perform linear interpolation across orientation bins. Histograms can also * use either signed or unsigned gradients. * * @param nbins * number of bins * @param histogramInterpolation * if true cyclic linear interpolation is used to share the * magnitude across the two closest bins; if false only the * closest bin will be filled. * @param orientationMode * the range of orientations to extract * @param strategy * the {@link SpatialBinningStrategy} to use to produce the * features */ public HOG(int nbins, boolean histogramInterpolation, FImageGradients.Mode orientationMode, SpatialBinningStrategy strategy) { this.extractor = new GradientOrientationHistogramExtractor(nbins, histogramInterpolation, orientationMode); this.strategy = strategy; } @Override public void analyseImage(FImage image) { extractor.analyseImage(image); } /** * Analyse the given image, but construct the internal data such that the * gradient magnitudes are multiplied by the given edge map before being * accumulated. This could be used to suppress all magnitudes except those * at edges; the resultant extracted histograms would only contain * information about edge gradients. * * @param image * the image to analyse * @param edges * the edge image */ public void analyseImage(FImage image, FImage edges) { extractor.analyseImage(image, edges); } /** * Compute the HOG feature for the given window. * * @param rectangle * the window * @return the computed HOG feature */ public Histogram getFeatureVector(Rectangle rectangle) { return currentHist = strategy.extract(extractor, rectangle, currentHist); } }





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