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The localized face from the previous stage is preprocessed to normalize the facial patterns. Several techniques, such as illumination gradient correction, were performed to compensate or to reduce the effect of lighting variations within the window where the face was detected. Also, histogram equalization was performed to reduce the nonuniformity in the pixel distributions that may occur due to various imaging situations. Additionally, normalization of the training set was performed to align all facial features (based on manual labeling) of the face with respect to a canonical template, so that they formed a good cluster in the high-dimensional feature space.



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Finally, every method that nondestructively modi es a Vector by returning a new one returns a non- xed-size Vector, even if the input Vector was xed-size. As you have seen in the examples, fast iteration for..in and for each..in loops works for Vectors just like it works for Arrays. Example 9-3 uses fast iteration and several of the methods of Vector to create a menu. EXAMPLE 9-3

12. Consider the formulas given in Problem 11. Suppose that we replace the 32-bit words X, Y ,and Z with 8-bit bytes x, y and z . Then we can compute the probabilities exactly. Write a computer program to verify the claimed probability in each part of Problem 11 using 8-bit words instead of 32-bit words.

package { import flash.display.Sprite; import flash.events.MouseEvent; import flash.text.TextField;

EXAMPLE 9-3

260 13. Verify equations (5.30) and (5.32).

(continued)





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The feature extraction stage is designed to obtain a meaningful representation of observations and also to reduce the dimension of the feature vector. It is assumed that a classifier that uses a smaller dimensional feature vector will run faster and use less memory, which is very desirable for any realtime system. Besides increasing accuracy by removing very specific information about the images, the feature extraction method also improves the computational speed of the classifier an important criterion for a real-time classifier system. In the proposed method, the following techniques were implemented to construct a feature vector from the observations. Experiments were conducted with the aim of achieving two goals, namely: to select the components of the PCA and NMF; and to make comparisons between PCA and NMF and use the suitable one for classification of age. A weighted NMF [10] scheme was used in the experimentation to alleviate the limitation of standard NMF in optimizing the local representation of the image. Several experiments were conducted with different numbers of bases to compare the classification results.

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14. The continuous approxiniation phase of the MD4 attack relies on the fact that for the functions F and G in (5.4) and (5.5), nearby inputs produce nearby outputs. Suppose that the 32-bit words A , B and C are rcplaced with 8-bit bytes a , b and c. Furthermore, suppose that a differs from u in exactly one randomly selected bit position, b differs from b in exactly one randomly selected bit position, and c differs from c in exactly one randomly selected bit position. Write computer programs to answer the following.

import flash.text.TextFieldAutoSize; import flash.text.TextFormat; public class ch9ex3 extends Sprite { protected var labels:Vector.<TextField>; protected const DEFAULT_FORMAT:TextFormat = new TextFormat("_sans", 12, 0x000000, true); protected const SELECTED_FORMAT:TextFormat = new TextFormat("_sans", 12, 0x00ffff, true); protected const MARGIN:Number = 20; public function ch9ex3() { labels = new Vector.<TextField>; makeMenuItem("home"); makeMenuItem("about"); makeMenuItem("blog"); makeMenuItem("contact"); } protected function makeMenuItem(label:String):void { var tf:TextField = new TextField(); tf.selectable = false; tf.defaultTextFormat = DEFAULT_FORMAT; tf.text = label; tf.width = tf.height = 0; tf.autoSize = TextFieldAutoSize.LEFT; if (labels.length > 0) { var lastTF:TextField = labels[labels.length-1]; tf.x = lastTF.x + lastTF.textWidth + MARGIN; } addChild(tf); labels.push(tf); tf.addEventListener(MouseEvent.CLICK, onItemClick); } protected function onItemClick(event:MouseEvent):void { for each (var tf:TextField in labels) { tf.setTextFormat((tf == event.target) SELECTED_FORMAT:DEFAULT_FORMAT); } } } }

To create a Vector, just use the Vector() constructor. The constructor takes two parameters: an initial length (which defaults to 0) and a determination of whether the Vector should be xed-size (which defaults to false). Its signature is:

a. Find the probability that F ( u , b. c ) and F(a , b , c ) differ in exactly k bit positions, for k = 0 , 1 , 2 , . . . .8.

Vector(length:uint = 0, fixed:Boolean = false)

You can type Vector literals into your code just like Array literals. Simply use the Vector literal syntax:

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