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FreeOCR is a free Optical Character Recognition Software for Windows and supports scanning from most Twain scanners and can also open most scanned PDF's and multi page Tiff images as well as popular image file formats. FreeOCR outputs plain text and can export directly to Microsoft Word format.

package { import com.actionscriptbible.Example; import flash.utils.Timer; import flash.events.TimerEvent; public class ch22ex3 extends Example { public static const TIMER_DELAY:Number = 1 * 1000; public var timer:Timer; public function ch22ex3() { timer = new Timer(TIMER_DELAY, 10); timer.addEventListener(TimerEvent.TIMER, onTimer); timer.addEventListener(TimerEvent.TIMER_COMPLETE, onTimerComplete); timer.start(); } protected function onTimer(event:TimerEvent):void { trace("Tick."); } protected function onTimerComplete(event:TimerEvent):void { trace("Ding!"); } } }

Tick. Tick. Tick. Tick. Tick. Tick. Tick. Tick. Tick. Tick. Ding!



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This chapter presents a new approach to the application of picture languages for cognitive analysis and reasoning of selected medical visualization. We will show new opportunities for applying these methods to undertake the task of automatic understanding of image semantics in intelligent medical information or computer-aided diagnosis systems. These systems are applied in various tasks supporting decisions taken in the wide area of health care and medical imaging. The possibility of obtaining information about the semantic content of medical images may contribute considerably to the creation of new intelligent cognitive medical systems. This chapter shows that structural techniques of artificial intelligence may be applied in the case of tasks related to automatic classification and machine perception of semantic pattern content, in order to determine the medical meaning of the images. We describe some examples presenting ways of applying such techniques in the creation of cognitive vision systems for selected classes of medical image.





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Now that you ve looked at how to set up a timer and listen to the events it dispatches, let s look at some practical ways to use these events. The Timer class res TimerEvent event objects. These events are ultimately not much different from the base Event class. No additional information is stored within a TimerEvent that s not in a

regular Event. You can get information about the Timer that red the event using the event object s target property. With the Timer reference, you can get more information such as the number of times the timer has red or the time between each tick, as shown in Example 22-4. EXAMPLE 22-4

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package { import com.actionscriptbible.Example; import flash.utils.Timer; import flash.events.TimerEvent; public class ch22ex4 extends Example { public static const TIMER_DELAY:Number = 0.5 * 1000; public var timer:Timer; public function ch22ex4() { timer = new Timer(TIMER_DELAY, 10); timer.addEventListener(TimerEvent.TIMER, onTimer); timer.addEventListener(TimerEvent.TIMER_COMPLETE, onTimerComplete); timer.start(); } protected function onTimer(event:TimerEvent):void { var timer:Timer = Timer(event.target); var timeElapsed:Number = timer.currentCount * timer.delay; var remainingCount:Number = timer.repeatCount - timer.currentCount; trace("Time elapsed :", timeElapsed / 1000, "seconds."); if (remainingCount > 0) { trace("There are", remainingCount, "ticks remaining."); } } protected function onTimerComplete(event:TimerEvent):void { trace("Ding!"); } } }

The event handler onTimer() prints out the time elapsed in seconds every time the TIMER event is red. The output of Example 22-4 should look something like this:

In this chapter, a new approach to the processing and analysis of medical images is proposed. We try to introduce the terms and methodology of medical data understanding as a new step along the way, starting from image processing, and followed by analysis and classification [1 5]. In the chapter we try to show that image-understanding technology, as the next step after image recognition, is useful (sometimes even necessary), possible and also effective. This will be done using four types of selected medical image. But in fact, the methods under consideration can also be used for many other kinds of medical image.

(3.2)

... There are 3 ticks remaining. Time elapsed : 4 seconds. There are 2 ticks remaining. Time elapsed : 4.5 seconds. There are 1 ticks remaining. Time elapsed : 5 seconds. Ding!

In the example, you useds three properties of the Timer class:

For this sequence, the first few steps of the Berlekamp-Massey Algorithm are illustrated in Table 3.2. For the periodic sequence ( 3 . 2 ) , the linear complexity is L = 6 (Problem 1 asks for the connection polynomial). Therefore, if we let 10011 be the initial fill of the LFSR corresponding to the connection polynomial determined by the Berlekamp-Massey Algorithm, the LFSR generates the sequencc s in (3.2). Here, we do not attempt to prove the validity of the Berlekarnp-Massey Algorithm. but we note in passing that the algorithm is closely related to the extended Euclidean Algorithm and continued fraction algorithms. We also note one important- but non-obvious--fact, namely, that any 2L consecutive bits can be used to completely determine a sequence that has linear complexity L. That is, after processing 2 L bits through the Berlekamp-

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