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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; } }

15-39. In Statistics for Research (John Wiley & Sons, 1983), S. Dowdy and S. Wearden presented the results of an experiment to measure the performance of hand-held chain saws. The experimenters measured the kickback angle through which the saw is de ected when it begins to cut a 3-inch stock synthetic board. Shown in the following table are

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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.

based on air samples taken randomly during one month of production: 7, 3, 4, 2, 5, 6, 9, 8, 7, 3, 4, 4, 3, 2, 6. Can you claim that the median uoride impurity level is less than 6 ppm State and test the appropriate hypotheses using the sign test with 0.05. What is the P-value for this test 15-46. Use the normal approximation for the sign test for the problem in Exercise 15-45. What is the P-value for this test 15-47. Consider the data in Exercise 10-42. Use the sign test with 0.05 to determine whether there is a difference in median impurity readings between the two analytical tests. 15-48. Consider the data in Exercise 15-43. Use the Wilcoxon signed-rank test for this problem with 0.05. What hypotheses are being tested in this problem 15-49. Consider the data in Exercise 15-45. Use the Wilcoxon signed-rank test for this problem with 0.05. What conclusions can you draw Does the hypothesis you are testing now differ from the one tested originally in Exercise 15-45 15-50. Use the Wilcoxon signed-rank test with 0.05 for the diet-modi cation experiment described in Exercise 10-41. State carefully the conclusions that you can draw from this experiment. 15-51. Use the Wilcoxon rank-sum test with 0.01 for the fuel-economy study described in Exercise 10-83. What

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.

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