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by exhaustive search in an appropriate range of values (see Figure 10.5). x and x are pairs of symmetric image pixels that have nonzero depth values. Observing that S( ) is a monotonic function of , we can considerably speedup this algorithm by adopting a hierarchical approach, which starts from a very sparse sampling of and proceeds with ner sampling in continuously decreasing intervals of the parameter value. The average error rate produced by the above technique is less than 2 [8]. Using the estimated pose parameters and nose-tip location, we de ne a local 3D coordinate frame, which is aligned with the face and centered on the nose tip. Pose compensation relies on warping the input depth image so that the local coordinate frame is aligned with a reference coordinate frame, thus bringing the face in upright orientation (warped image). The reference frame is de ned during training, as will be described below. The alignment between the two frames is further re ned to pixel accuracy by applying the ICP surface registration algorithm [19] between the warped and a reference depth image of the same subject.

If a function is declared more than once in the same file, the linkage directive can appear on every declaration It can also appear only on the first declaration of the function, in which case the second and following declarations receive the linkage specified by the linkage directive on the first declaration For example:

// ---- myMathh ---extern "C" double calc( double ); // ---- myMathC ---// declaration of calc() in myMathh #include "myMathh" // defines the extern "C" calc() function // calc() can be called from C programs double calc( double dparm ) { //

Figure 10.5. Estimation of the plane of bilateral face symmetry. The recti ed images Ir obtained for different values of and the computed symmetry cost S( ) are shown.

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iter = find( slistbegin(), slistend(), s_elem ); // generates: find the first occurrence of Sonata32 found! cout "find the first occurrence of " s_elem < "\t" ( iter != slistend() "found!\n" : "not found!\n" ); } find_if() template< class InputIterator, class Predicate > InputIterator find_if( InputIterator first, InputIterator last, Predicate pred );

As a result, accounting data loss will occur due to device reboots, but not due to packet loss or network failures of short duration Note that while transfer efficiency will increase with batch size, without non-volatile storage the potential data loss from a device reboot will also increase Through implementation of a scheduling algorithm, event-driven systems with batching can deliver appropriate service to accounting events that require low processing delays For example, high-value inter-domain accounting events could be sent immediately, thus enabling use of fraud-prevention techniques, while all other events would be batched As a result, this approach can have good scalability and flexibility characteristics In the event-driven polling model, an accounting manager will poll the device for accounting data only when it receives an event Events are typically generated by accounting clients.

The resulting depth image contains missing pixels, some of which are lled by exploiting the face symmetry and using depth values of mirroring pixels. Remaining missing pixels are linearly interpolated from neighboring points by means of a 2D Delaunay triangulation. The interpolated depth image is nally used to rectify the associated color image, thus resulting in a pair of canonical frontal views. Figure 10.6 illustrates several examples of pose compensation. For the training, a similar but simpler pose compensation technique is used. The face orientation is estimated by manually selecting three points on the input image, which de ne a local 3D coordinate frame. Then, the input color and depth images are warped to align this local coordinate frame with the coordinate frame of the camera, using the surface interpolation algorithm described above. For one of the pose-compensated depth images of each person, a simpli ed version of the automatic pose estimation algorithm described above is applied, thus estimating a reference coordinate frame. This last step is important, since the slant of the nose differs from person to person.

The elements in the range marked off by [first,last) are examined in turn, and pred is applied to each If pred evaluates to true, the search ends find_if() returns an InputIterator to the element If no match is found, last is returned

#include <algorithm> #include <list> #include <set> #include <string> #include <iostreamh> // provides an alternative equality operator // returns true if string is contained with the // member object friend set class OurFriends { public: bool operator()( const string& str ) { return ( friendsetcount( str )); } static void FriendSet( const string *fs, int count ) { copy( fs, fs+count, inserter( friendset, friendsetend() )); } private: static set< string, less<string>, allocator > friendset; }; set< string, less<string>, allocator > OurFriends::friendset; int main() { string Pooh_friends[] = { "Piglet", "Tigger", "Eyeore" }; string more_friends[] = { "Quasimodo", "Chip", "Piglet" }; list<string,allocator> lf( more_friends, more_friends+3 ); // populate a list of pooh friends OurFriends::FriendSet( Pooh_friends, 3 ); list<string,allocator>::iterator our_mutual_friend; our_mutual_friend = find_if( lfbegin(), lfend(), OurFriends()); // generates: // Ah, imagine our friend Piglet is also a friend of Pooh if ( our_mutual_friend != lfend() ) cout "Ah, imagine our friend " *our_mutual_friend

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