Note in Java

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Note
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Under certain conditions, 3 3 convolution matrices are accelerated if your processor supports a certain extended instruction set. Check the AS3LR under the matrix property of ConvolutionFilter for details.
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Example 37-12 shows a few useful convolution matrices.
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EXAMPLE 37-12
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http://actionscriptbible.com/ch37/ex12
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Multiple Convolution Matrices
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package { import flash.display.Sprite; import flash.filters.ConvolutionFilter; public class ch37ex12 extends Sprite { function ch37ex12() { var a:TestImage = new TestImage(0.5, var b:TestImage = new TestImage(0.5, var c:TestImage = new TestImage(0.5, var d:TestImage = new TestImage(0.5, addChild(a); addChild(b); addChild(c); addChild(d);
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0, 0); 250, 0); 0, 167); 250, 167);
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continued
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Part VIII: Graphics Programming and Animation
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EXAMPLE 37-12
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(continued)
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var aMat:Array = [0, 1, 0, 1, -4, 1, 0, 1, 0]; //edge detect a.filters = [new ConvolutionFilter(3, 3, aMat)]; var bMat:Array = [ 0, -1, 0, -1, 5, -1, 0, -1, 0]; //sharpen b.filters = [new ConvolutionFilter(3, 3, bMat)]; var cMat:Array = [1, 1, 1, 1, 1, 1, 1, 1, 1]; //blur c.filters = [new ConvolutionFilter(3, 3, cMat, 9)]; var dMat:Array = [-2, -1, 0, -1, 1, 1, 0, 1, 2]; //emboss d.filters = [new ConvolutionFilter(3, 3, dMat)]; } } } import flash.display.Loader; import flash.net.URLRequest; import flash.system.LoaderContext; import flash.events.MouseEvent; import flash.geom.Point; class TestImage extends Loader { protected var scale:Number; protected var origin:Point; public function TestImage(scale:Number = 1, x:Number = 0, y:Number = 0) { //photo (CC-BY) Roger Braunstein //source http://www.flickr.com/photos/rogerimp/2940373537/ var url:String = "http://actionscriptbible.com/files/heiwadoori.jpg"; load(new URLRequest(url), new LoaderContext(true)); this.scale = scale; this.origin = new Point(x, y); addEventListener(MouseEvent.MOUSE_DOWN, onMouseDown); addEventListener(MouseEvent.MOUSE_UP, onMouseUp); onMouseUp(null); } protected function onMouseDown(event:MouseEvent):void { parent.setChildIndex(this, parent.numChildren-1); scaleX = scaleY = 1; x = y = 0; } protected function onMouseUp(event:MouseEvent):void { scaleX = scaleY = scale; x = origin.x; y = origin.y; } }
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37: Applying Filters
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The rst matrix, aMat, enhances an image so that edges are highlighted (usually in crazy neon colors.) Think a bit about how this is done. A pixel s four neighbors in every direction are added up, and the pixel is subtracted away. When the neighbors up, down, left, and right are close to the input pixel value, the four positive pixels and the 4x pixel should cancel out. But when the color changes abruptly in one or more of those directions, the difference between the sum and the center pixel is signi cant. So areas of little change appear black, and areas where the color changes abruptly in one of the four cardinal directions get colored. You can perform more simple edge detection biased for a single direction by balancing out the center pixel and its neighbor in one direction with opposite coef cients, setting all other elements to zero. The bMat matrix works in almost the same way, except that the center pixel is weighted one more than the sum of the other pixels so the original colors are preserved and added onto rather than subtracted away. Where there is little localized contrast, additional brightness is sucked away by the four 1x pixels on the sides, leaving at minimum the input value remaining. Where there is a lot of local contrast, the center pixel is multiplied in brightness: with less than a full 1x contribution coming from each of the four neighboring pixels, more of the center pixel s 5x coef cient remains. So pixels where the neighbors are of different brightness are enhanced, which appears to sharpen the image. The cMat matrix produces a simple Gaussian blur, much like a BlurFilter can provide. A Gaussian blur averages neighboring pixels within a given radius. To blur more or less, you would create matrices of the same form with greater or smaller sizes. A larger matrix takes into account pixels farther away, thus increasing the radius or blurriness of the blur. Note that rather than set each coef cient to 1/9, I ve written in all the coef cients as 1 and used a divisor of 9. This saved me some typing. Finally, dMat produces a cheesy-looking emboss. By inversely removing contributions from the upper-left quadrant of neighbors and adding contributions from the bottom-right quadrant of neighbors, you emphasize pixels where the contrast is high in one direction, as if they are lit up by light coming from that direction in this case, a diagonal from bottom right to top left. So that you can look at the results in more detail, holding down the mouse button on any of the images scales the image up temporarily. These lters, and others you can perform with a convolution lter, may not be high quality, but they are excellent for preparing images and video for analysis by computer vision algorithms. They run fast and can emphasize certain local features of images that may be important.
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