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For Macintosh users who do not have iMovie, Apple offers a $49 downloadable version for purchase on its Web site (wwwapplecom/imovie) As of this writing, iMovie is not currently available for PC users, but you might find a package with the same kind of simplicity Professionals who want control over every last nuance of the editing will find iMovie limited However, I think that if you really sat down and looked at how people edit, you would find that iMovie covers 95 percent of the basic and professional users out there This statement will probably bring me e-mail, but it is true iMovie is a great quick-and-dirty video tool Apple lists iMovie-compatible camcorders on its Web site (wwwapplecom/ imovie/shoothtml) Most common camcorders are fine, but some have quirks that are documented on the site Camcorders that are not on the list might or might not work Digital video discussion forums are full of conflicting information about iMovie compatibility with unlisted camcorders A little advance research can save you frustration later If you do not have a digital camcorder with FireWire capability, you can buy a FireWire conversion box Sony offers a device (the affectionately titled DVMC-DA2) that is currently compatible with iMovie In my work with the Sony conversion boxes, I have found that they are not as convenient as I would like The conversion boxes do not give you the full control you would have with a compatible digital camcorder

Figure 231 Rough Set Illustration Rough set theory is based on the assumption that some information, or knowledge, about the elements of the universe of discourse is initially available This is contrary to fuzzy set theory where no such prior information is assumed The information available about elements is used to nd similar elements and indiscernible elements Rough set theory is then based on the concepts of upper and lower approximations of sets (refer to Figure 231) The lower approximation contains those elements that belong to

onClipEvent(load) { //enter the value which you scaled //your bigPic for //the magnification (eg 15=150%) magnification=2; lastX=_x; lastY=_y; } onClipEvent(enterFrame) { //find out how much the magnifier has //moved and //multiply the distance by the //magnification bigPicMoveX=(lastX-_x) *magnification; bigPicMoveY=(lastY-_y) *magnification; //remember where the magnifier was //for next move lastX=_x; lastY=_y; //move the location of bigPic which //is under the mask bigPic_x=bigPic_x+bigPicMoveX; bigPic_y=bigPic_y+bigPicMoveY; }

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Computational Intelligence: An Introduction, Second Edition AP Engelbrecht c 2007 John Wiley & Sons, Ltd

The first time you launch iMovie, you are given the opportunity to run the tutorial that comes with the software You also can access the tutorial directly by pulling down the Help menu while running iMovie In this chapter, I will cover the basics of working with iMovie They will be enough to get you started with putting together a very simple video piece for streaming However, I highly encourage you to spend the short amount of time that it takes to go through the iMovie tutorial before you do anything else The software is very easy to use Following Apple s well-written tutorial will make you that much more confident with iMovie in the long run

the set with full certainty, while the upper approximation encapsulates elements for which membership is uncertain The boundary region of a set, which is the di erence between the upper and lower approximations, thus contains all examples which cannot be classi ed based on the available information Rough sets have been shown to be fruitful in a variety of application areas, including decision support, machine learning, information retrieval and data mining What makes rough sets so desirable for real-world applications is their robustness to noisy environments, and situations where data is incomplete It is a supervised approach, which clari es the set-theoretic characteristics of classes over combinatorial patterns of the attributes In doing so, rough sets also perform automatic feature selection by nding the smallest set of input parameters necessary to discern between classes The idea of discernibility is de ned in Section 231, based on the formal de nition of a decision system Section 232 shows how rough sets treat vagueness by forming a boundary region, while Section 233 discusses the treatment of uncertainty in the implementation of the rough membership function

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