org.openimaj.image.processing.convolution.BasicDerivativeKernels 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,
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 *
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 * 	this list of conditions and the following disclaimer in the documentation
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 * 	software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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package org.openimaj.image.processing.convolution;

import org.openimaj.image.FImage;

/**
 * A set of standard derivative kernels. These kernels help estimate the derivative over various orders at a point in a matrix. 
 * This is approximated by applying a finite difference derivative operation on a gaussian kernel with a very low sigma. i.e. a gaussian
 * kernel that looks like:
 * 
 * [
 * 	[0,0,0],
 *  [0,1,0],
 *  [0,0,0]
 * ]
 * 
 * By successive derivative calculations in the x direction and y direction it is possible to estimate derivatives in both directions as well.
 * 
 * @author Jonathon Hare ([email protected])
 *
 */
public class BasicDerivativeKernels {
	static class DxKernel extends FConvolution {
		public DxKernel() { super(new FImage(new float[][] {{-0.5f,0,0.5f}})); }
	}
	
	static class DyKernel extends FConvolution {
		public DyKernel() { super(new FImage(new float[][] {{-0.5f}, {0}, {0.5f}})); }
	}

	static class DxxKernel extends FConvolution {
		public DxxKernel() { super(new FImage(new float[][] {{1,-2,1}})); }
	}
	
	static class DxyKernel extends FConvolution {
		public DxyKernel() { super(new FImage(new float[][] {{0.25f,0,-0.25f}, {0,0,0}, {-0.25f,0,0.25f}})); }
	}
	
	static class DyyKernel extends FConvolution {
		public DyyKernel() { super(new FImage(new float[][] {{1}, {-2}, {1}})); }
	}
	
	static class DxxxxKernel extends FConvolution {
		public DxxxxKernel() { super(new FImage(new float[][] {{1,-4 ,6 ,-4 ,1}})); }
	}
	
	static class DyyyyKernel extends FConvolution {
		public DyyyyKernel() { super(new FImage(new float[][] {{1}, {-4},{6},{-4},{1}})); }
	}
	
	static class DxxyyKernel extends FConvolution {
		public DxxyyKernel() { super(new FImage(new float[][] {{1f,-2f,1f},{-2f,4f,-2f},{1f,-2f,1f}})); }
	}
	
	/**
	 * kernel approximating the first derivative of a low-sigma gaussian in the x-direction [-0.5, 0, 0.5]. 
	 * Useful for giving an estimate of the second derivative in x of any given point
	 */
	public static final FConvolution  DX_KERNEL  = new DxKernel();
	
	/**
	 * kernel approximating the first derivative of a low-sigma gaussian in the y-direction [-0.5, 0, 0.5]'. 
	 * Useful for giving an estimate of the second derivative in y of any given point
	 */
	public static final FConvolution DY_KERNEL  = new DyKernel();
	
	/**
	 * kernel approximating the second derivative of a low sigma gaussian in the x-direction [1, -2, 1]. 
	 * Useful for giving an estimate of the second derivative in x of any given point
	 */
	public static final FConvolution DXX_KERNEL = new DxxKernel();
	
	/**
	 * kernel approximating the first derivative of a low sigma gaussian in the x-direction and y-direction [[-0.25, 0, 0.25], [0, 0, 0], [0.25, 0, -0.25]] . 
	 * Useful for giving an estimate of the first order derivative in x then y of any given point
	 */
	public static final FConvolution DXY_KERNEL = new DxyKernel();
	
	/**
	 * kernel approximating the second derivative of a low sigma gaussian in the y-direction [1, -2, 1]'. 
	 * Useful for giving an estimate of the second derivative in y of any given point
	 */
	public static final FConvolution DYY_KERNEL = new DyyKernel();
	
	
	/**
	 * kernel approximating the fourth derivative of a low sigma gaussian in the x-direction [1,-4,6,-4,1]^T
	 * Useful for giving an estimate of the fourth derivative in y of any given point
	 */
	public static final FConvolution DXXXX_KERNEL = new DxxxxKernel();
	
	/**
	 * kernel approximating the second derivative of a low sigma gaussian in the x-direction and y-direction [[1,-2,1],[-2,4,-2],[1,-2,1]] . 
	 * Useful for giving an estimate of the second order derivative in x then y of any given point
	 */
	public static final FConvolution DXXYY_KERNEL = new DxxyyKernel();
	/**
	 * kernel approximating the fourth derivative of a low sigma gaussian in the y-direction [1,-4,6,-4,1]^T
	 * Useful for giving an estimate of the fourth derivative in y of any given point
	 */
	public static final FConvolution DYYYY_KERNEL = new DyyyyKernel();
}