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In this section we describe an illumination compensation algorithm, which generates from the input image a novel image relit from a frontal direction. Our approach is inspired by work on image-based scene relighting used for rendering realistic images [54]. Image relighting relies on inverting the rendering equation this is, the equation that relates the image brightness with the object material and geometry and the illumination of the scene. Given several images of the scene under different conditions, this equation may be solved (although an ill-posed problem) to recover the illumination distribution and then use this to re-render the scene under novel illumination. The rst step is therefore to recover the scene illumination from a pair of color and depth images. Assuming that the scene is illuminated by a single light source, a

" is also a friend of Pooh\n"; return 0; } find_end() template< class ForwardIterator1, class ForwardIterator2 > ForwardIterator1 find_end( ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2 ); template< class ForwardIterator1, class ForwardIterator2, class BinaryPredicate > ForwardIterator1 find_end( ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2, BinaryPredicate pred );

The sequence marked off by [first1,last1) is searched for the last occurrence of a second sequence marked off by the iterator pair [first2,last2) For example, given the character sequence Mississippi and a second sequence ss, find_end() returns a ForwardIterator to the first s of the second ss sequence If the second sequence is not found within the first, last1 is returned In the first version, the equality operator of the underlying type is used In the second version, a binary predicate operation passed in by the user is applied

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#include <algorithm> #include <vector> #include <iostreamh> #include <asserth> int main() { int array[ 17 ] = { 7,3,3,7,6,5,8,7,2,1,3,7,6,3,8,4,3 }; int subarray[ 3 ] = { 3, 7, 6 }; int *found_it; // find the last occurrence of the sequence 3,7,6 // in array, and return address of first element found_it = find_end( &array[0], &array[17], &subarray[0], &subarray[3] ); assert( found_it == &array[10] ); vector< int, allocator > ivec( array, array+17 ); vector< int, allocator > subvec( subarray, subarray+3 ); vector< int, allocator >::iterator found_it2; found_it2 = find_end( ivecbegin(), ivecend(), subvecbegin(), subvecend(), equal_to<int>() ); assert( found_it2 == ivecbegin()+10 ); cout "ok: find_end correctly returned beginning of " < "last matching sequence: 3,7,6!\n"; } find_first_of() template< class ForwardIterator1, class ForwardIterator2 > ForwardIterator1 find_first_of( ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2 ); template< class ForwardIterator1, class ForwardIterator2, class BinaryPredicate >

Examples of events include: when a batch of a given size has been gathered, when data of a certain type are available or lapse of a minimum time period Without non-volatile storage, an event-driven polling model will lose data due to device reboots, but is resistant to packet loss, or network partitions of short duration due to its buffering nature Unless a minimum delivery interval is set, event-driven polling systems are not useful in monitoring of device health The event-driven polling model can be suitable for use in roaming, since it permits accounting data to be sent to the roaming partners with low processing delay At the same time, non-roaming accounting can be handled via more efficient polling techniques, thereby providing the best of both worlds Where batching can be implemented, the state required in event-driven polling can be reduced to scale with the number of active devices.

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (1024 / 1065) [2001-3-29 11:32:15]

technique is adopted that learns the nonlinear relationship between the image brightness and light source direction L using a set of arti cially generated bootstrap images. For each subject in the database, we use the reference pose compensated depth image Ir to render N virtual views of the face, illuminated from different known directions. To decrease the dimensionality of the problem, from each rendered image we extract a feature vector qi , i = 1, . . . , N, comprised of locally weighted averages of image brightness over M = 30 locations, which were chosen to include face pixels with similar albedo. The normalized samples are subsequently used to approximate the M-dimensional illuminant direction function L = Gj (q) of subject j using support vector machines [55]. Given a pose compensated color image Ic and a claimed ID j, the light source direction L can be computed by the previous formula, after extracting the feature vector q from Ic . The next step is to relight the color image with a frontal light L0 . To do this, rst we approximate the image brightness in each pixel u using the formula Ic (u) = A(u)R(Id , L, u), (10.5)

ForwardIterator1 find_first_of( ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2, BinaryPredicate pred );

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