Difference between revisions of "Example Image Derivative"
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Revision as of 12:55, 19 September 2015
Example of how to compute different image derivatives. The gradient (1st order derivative) is probably the important image derivative and is used as a first step when extracting many types of image features. The code below shows how gradient, Hessian (2nd order), and arbitrary image derivatives can be computed.
Example Code:
Concepts:
- Image Derivative
- Gradient
- Hessian
Relevant Applets:
Example Code
/**
* Example showing how to compute different image derivatives using built in functions.
*
* @author Peter Abeles
*/
public class ExampleImageDerivative {
public static void main(String[] args) {
BufferedImage input = UtilImageIO.loadImage("../data/evaluation/simple_objects.jpg");
// We will use floating point images here, but ImageUInt8 with ImageSInt16 for derivatives also works
ImageFloat32 grey = new ImageFloat32(input.getWidth(),input.getHeight());
ConvertBufferedImage.convertFrom(input, grey);
// First order derivative, also known as the gradient
ImageFloat32 derivX = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivY = new ImageFloat32(grey.width,grey.height);
GImageDerivativeOps.gradient(DerivativeType.SOBEL, grey, derivX, derivY, BorderType.EXTENDED);
// Second order derivative, also known as the Hessian
ImageFloat32 derivXX = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivXY = new ImageFloat32(grey.width,grey.height);
ImageFloat32 derivYY = new ImageFloat32(grey.width,grey.height);
GImageDerivativeOps.hessian(DerivativeType.SOBEL, derivX, derivY, derivXX, derivXY, derivYY, BorderType.EXTENDED);
// There's also a built in function for computing arbitrary derivatives
AnyImageDerivative<ImageFloat32,ImageFloat32> derivative =
GImageDerivativeOps.createAnyDerivatives(DerivativeType.SOBEL, ImageFloat32.class, ImageFloat32.class);
// the boolean sequence indicates if its an X or Y derivative
derivative.setInput(grey);
ImageFloat32 derivXYX = derivative.getDerivative(true, false, true);
// Visualize the results
ListDisplayPanel gui = new ListDisplayPanel();
gui.addImage(ConvertBufferedImage.convertTo(grey,null),"Input Grey");
gui.addImage(VisualizeImageData.colorizeSign(derivX, null, -1),"Sobel X");
gui.addImage(VisualizeImageData.colorizeSign(derivY, null, -1),"Sobel Y");
// Use colors to show X and Y derivatives in one image. Looks pretty.
gui.addImage(VisualizeImageData.colorizeGradient(derivX, derivY, -1),"Sobel X and Y");
gui.addImage(VisualizeImageData.colorizeSign(derivXX, null,-1),"Sobel XX");
gui.addImage(VisualizeImageData.colorizeSign(derivXY, null,-1),"Sobel XY");
gui.addImage(VisualizeImageData.colorizeSign(derivYY, null,-1),"Sobel YY");
gui.addImage(VisualizeImageData.colorizeSign(derivXYX, null,-1),"Sobel XYX");
ShowImages.showWindow(gui,"Image Derivatives",true);
}
}