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A histogram shows the distribution of colors in an image. In Flash Player, a bitmap s histogram is provided for each of the four channels individually. Each channel s histogram is a plot of the luminance value (between 0 and 255) and its frequency the number of pixels in the image that have that value for the speci ed channel. You can use a histogram to quickly get a glance of the dominant colors in the image and their luminances. By summing up the contributions from the RGB channels, you can look at the distribution of light levels in an image, too. This information is useful in computer vision algorithms, often as a rst-pass analysis. For instance, when attempting to track a color marker, you can look for a signi cant amount of the target color in the image; if it doesn t appear, you can save processor time by not even attempting to locate the marker. When using a histogram to do image analysis, you ll likely want to smooth out the data to minimize impact of noise and artifacts. And you ll usually be looking for local and global minima and maxima to identify dominant colors or brightnesses. In object recognition algorithms, you may also want to retrieve the histogram of a subset of the image to try to zero in on the location of the object. Retrieve the histogram of an image, or a region of interest (ROI) within an image, by calling the histogram() method. The method optionally takes a Rectangle de ning the region of interest. It returns a Vector of Vectors of Numbers in other words, four histograms, one for each channel, each with 256 frequencies, indicating the number of pixels found in the bitmap with the corresponding brightness.



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Q1 =

FP10. You can use the histogram() method in Flash Player 10 and later. In Flash Player 9, you can generate a histogram manually with pixel-by-pixel tallying of the entire image.

the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. SVM has now evolved into an active area of research [18 21]. This chapter will first introduce the major feature extraction methods LDA and PCA. The MCE algorithm for integrated feature extraction and classification and the nonlinear formulation of SVM are then introduced. Feature extraction and compression with MCE and SVM are discussed subsequently. The performances of these feature extraction and classification algorithms are compared and discussed based on the experimental results on Deterding vowels and TIMIT continuous speech databases.





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In Example 36-10, you ll draw a live histogram over the webcam s image, in much the same way that you d write a spectrum visualizer for sound.

that is, 2 is the new Q1 that results from the modified X1 computed in step 16. Since no conditions were specified on Q1, we will not violate any previous conditions by letting Q1 = 2. Next, recall that

EXAMPLE 36-10

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package { import flash.display.*; import flash.events.Event; import flash.media.Camera; import flash.media.Video; [SWF(width="500",height="500",frameRate="20")] public class ch36ex10 extends Sprite { protected const COLORS:Vector.<uint> = new <uint>[0xff0000, 0xff00, 0xff]; protected const SCALE:Number = 200 / 256; protected var bmp:BitmapData; protected var video:Video; protected var hstR:Shape, hstG:Shape, hstB:Shape; protected var allHstShapes:Vector.<Shape>; public function ch36ex10() { bmp = new BitmapData(stage.stageWidth, stage.stageHeight); video = new Video(stage.stageWidth, stage.stageHeight); video.attachCamera(Camera.getCamera()); addChild(video); stage.addEventListener(Event.ENTER_FRAME, onEnterFrame); var hstbox:Sprite = new Sprite(); addChild(hstbox); hstbox.x = stage.stageWidth - 270 - 10; hstbox.y = 10; hstbox.graphics.lineStyle(0, 0xffffff); hstbox.graphics.beginFill(0, 0.5); hstbox.graphics.drawRect(0, 0, 270, 150); hstR = new Shape(); hstG = new Shape(); hstB = new Shape(); allHstShapes = new <Shape>[hstR, hstG, hstB]; for each (var hstShape:Shape in allHstShapes) { hstbox.addChild(hstShape); hstShape.y = hstbox.height - 10; hstShape.blendMode = BlendMode.ADD; hstShape.rotationY = 36; hstShape.scaleY = -1; } hstR.x = 10; hstG.x = 30; hstB.x = 50; } protected function onEnterFrame(event:Event):void { bmp.draw(video); var allHstData:Vector.<Vector.<Number>> = bmp.histogram(bmp.rect); var PIXELCOUNT:int = bmp.width*bmp.height; for (var i:int = 0; i < allHstShapes.length; i++) { var hstData:Vector.<Number> = allHstData[i]; var g:Graphics = allHstShapes[i].graphics; g.clear();

Q2 =

g.beginFill(COLORS[i]); for (var x:int = 0; x < hstData.length; x++) { g.drawRect(x*SCALE, 0, SCALE, hstData[x]/PIXELCOUNT * 1500*SCALE); } } } } }

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