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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.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
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 * 	this list of conditions and the following disclaimer.
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 *   *	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.
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 *   *	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.
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 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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package org.openimaj.image.saliency;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.image.FImage;
import org.openimaj.image.analysis.algorithm.HorizontalProjection;
import org.openimaj.image.analysis.algorithm.VerticalProjection;
import org.openimaj.math.geometry.shape.Rectangle;

/**
 * Extract the subject region of an image based on the
 * part that is least blurred (most in-focus).
 * 

* Algorithm based on: * Yiwen Luo and Xiaoou Tang. 2008. * Photo and Video Quality Evaluation: Focusing on the Subject. * In Proceedings of the 10th European Conference on Computer Vision: * Part III (ECCV '08), David Forsyth, Philip Torr, and Andrew Zisserman (Eds.). * Springer-Verlag, Berlin, Heidelberg, 386-399. DOI=10.1007/978-3-540-88690-7_29 * http://dx.doi.org/10.1007/978-3-540-88690-7_29 *

* Note that this is not scale invariant - you will get different results with * different sized images... * * @author Jonathon Hare ([email protected]) */ @Reference( type = ReferenceType.Inproceedings, author = { "Luo, Yiwen", "Tang, Xiaoou" }, title = "Photo and Video Quality Evaluation: Focusing on the Subject", year = "2008", booktitle = "Proceedings of the 10th European Conference on Computer Vision: Part III", pages = { "386", "", "399" }, url = "http://dx.doi.org/10.1007/978-3-540-88690-7_29", publisher = "Springer-Verlag", series = "ECCV '08", customData = { "isbn", "978-3-540-88689-1", "location", "Marseille, France", "numpages", "14", "doi", "10.1007/978-3-540-88690-7_29", "acmid", "1478204", "address", "Berlin, Heidelberg" } ) public class LuoTangSubjectRegion implements SaliencyMapGenerator { DepthOfFieldEstimator dofEstimator; Rectangle roi; private FImage dofMap; private float alpha = 0.9f; /** * Construct with default values for the {@link DepthOfFieldEstimator} * and an alpha parameter of 0.9. */ public LuoTangSubjectRegion() { dofEstimator = new DepthOfFieldEstimator(); } /** * Construct with the given parameters. * @param alpha the alpha value. * @param maxKernelSize Maximum kernel size for the {@link DepthOfFieldEstimator}. * @param kernelSizeStep Kernel step size for the {@link DepthOfFieldEstimator}. * @param nbins Number of bins for the {@link DepthOfFieldEstimator}. * @param windowSize window size for the {@link DepthOfFieldEstimator}. */ public LuoTangSubjectRegion(float alpha, int maxKernelSize, int kernelSizeStep, int nbins, int windowSize) { this.dofEstimator = new DepthOfFieldEstimator(maxKernelSize, kernelSizeStep, nbins, windowSize); this.alpha = alpha; } /* (non-Javadoc) * @see org.openimaj.image.analyser.ImageAnalyser#analyseImage(org.openimaj.image.Image) */ @Override public void analyseImage(FImage image) { image.analyseWith(dofEstimator); dofMap = dofEstimator.getSaliencyMap(); for (int y=0; y





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