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computed near the palm center cp and corresponds to the palm radius (Figure 109c) Finally, the 2D orientation of the hand is given by the slant of the vertical image axis with the hand forearm axis The latter is approximated by the principal eigenvector of the covariance matrix of the 2D hand forearm pixels belonging to the mask To detect the ngers, rst we draw circular homocentric arcs ([ /2, + /2]) with increasing radius r (r [12rp , 4rp ]) around cp and obtain a set of circular segments of the ngers After excluding segments with very large or very small length compared to the average nger width, we use the midpoints of remaining segments to estimate the nger skeletons To do this, we should cluster skeleton points belonging to the same nger A minimum spanning tree having as vertices these midpoints is created.

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When we include this instance in our program, the compound assignment of 1, above, executes correctly (This is an example of providing an overloaded operator for a class type Operator overloading is discussed in detail in 15)

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (156 / 1065) [2001-3-29 11:32:04]

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By discarding edges that are longer than a threshold, we obtain an initial set of clusters, which is further re ned by omitting clusters with very few points (less than three) and merging clusters that satisfy a proximity criterion This criterion is based on the collinearity of the cluster points and is formulated as < ( 1 + 2 ), where 1 is the error of tting a 2D line on the points of the rst cluster, 2 for the second cluster, for their union, and a constant (we set = 1) The four longer clusters are retained These correspond to the four ngers, excluding the thumb Approximate skeletons of ngers are then obtained by tting 2D line segments on the points of each cluster In addition, we can locate right and left nger boundaries by searching in a direction perpendicular to the nger skeleton.

Using the preceding definition as a model, provide implementations of the other three compound operators for complex<double> Add them to the following small program and execute it

10.4.2 Lightweight Extensible Authentication Protocol (LEAP)

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#include <iostream> #include <complex> // compound operator definitions go here int main() { complex< double > cval( 40, 10 ); cout cval endl; cval += 1; cout cval endl; cval -= 1; cout cval endl; cval *= 2; cout cval endl; cout /= 2; cout cval endl; }

Exercise 410 The Standard C++ complex number type does not provide an increment operator, although its absence is not intrinsic to the nature of complex numbers after all,

Unlike techniques that use the hand silhouette to detect the ngers, our approach is ef cient even for noisy images with disconnected boundaries (eg, due to rings) Moreover, it is much faster since it operates on a small set of image pixels and not the whole image The various steps of this algorithm are illustrated in Figure 1010a c..

cval += 1;

in effect increments cval's real part by 1 Provide a definition of the increment operator, add it to the following program, and then compile and execute:

The nger localization algorithm described in the previous section relies solely on 3D information, thus being sensitive in missing depth pixels along nger boundaries. In order to estimate nger boundaries more accurately, we also use the color image.

#include <iostream> #include <complex> // increment operator definitions go here int main() { complex< double > cval( 40, 10 ); cout cval 'endl'; ++cval; cout cval endl; }

The conditional operator provides a convenient notational alternative to simple if-else statements For example, rather than write

Figure 10.10. Finger detection and localization. (a) Original depth image, (b) circular arcs used to

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (157 / 1065) [2001-3-29 11:32:04]

To facilitate wireless network access for mobile users, Cisco developed a lightweight EAP authentication variant that allows a user to use its Cisco wireless LAN (WLAN) network interface card (NIC) to perform a simple Windows-type user name and password login to gain access to the network as if it was connected through wired Ethernet. The result is what is called Lightweight EAP, LEAP, that can support both Windows Active Directory databases and RADIUS infrastructures. LEAP takes some ideas from the Microsoft version of CHAP, commonly known as MS-CHAP in that it uses a challenge/response mechanism for authentication. However, LEAP provides a mutual authentication, through which both the authentication server and the client challenge each other. From a protocol standpoint, on the client side, LEAP is designed based on IEEE 802.1X standards and integrates well with the 802.11 MAC procedures such as the association process. On the wired side, LEAP attempted to be consistent with the earlier specification of transport of EAP over RADIUS [RADEXT2869]. LEAP implements the Wired Equivalent Privacy (WEP) security mechanism, which is a symmetric key-based encryption mechanism to protect traffic between the client and the WLAN AP. We will not go through WEP in this text due to the massive criticism that WEP has received. Although LEAP uses WEP for wireless link encryption, it provides its own key

bool is_equal = false; if ( !strcmp( str1, str2 )) is_equal = true;

detect nger segments, (c) 2D lines tted on skeleton points and boundaries, (d) estimated nger boundary segments.

bool is_equal = !strcmp( str1, str2 ) true : false;

The conditional operator has the following syntactic form:

expr1 expr2 : expr3;

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