cvflann::L2< T > Struct Template Reference

#include <dist.h>

Public Types
typedef True	is_kdtree_distance
typedef True	is_vector_space_distance
typedef T	ElementType
typedef Accumulator< T >::Type	ResultType

Public Member Functions
template<typename Iterator1 , typename Iterator2 >
ResultType	operator() (Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist=-1) const
template<typename U , typename V >
ResultType	accum_dist (const U &a, const V &b, int) const

Detailed Description

template<class T>
struct cvflann::L2< T >

Squared Euclidean distance functor, optimized version

Member Typedef Documentation

template<class T>

typedef T cvflann::L2< T >::ElementType

template<class T>

typedef True cvflann::L2< T >::is_kdtree_distance

template<class T>

typedef True cvflann::L2< T >::is_vector_space_distance

template<class T>

typedef Accumulator<T>::Type cvflann::L2< T >::ResultType

Member Function Documentation

template<class T>

template<typename U , typename V >

ResultType cvflann::L2< T >::accum_dist ( const U &  a, const V &  b, int    ) const

inline

Partial euclidean distance, using just one dimension. This is used by the kd-tree when computing partial distances while traversing the tree.

Squared root is omitted for efficiency.

template<class T>

template<typename Iterator1 , typename Iterator2 >

ResultType cvflann::L2< T >::operator() ( Iterator1  a, Iterator2  b, size_t  size, ResultType  worst_dist = -1  ) const

inline

Compute the squared Euclidean distance between two vectors.

This is highly optimised, with loop unrolling, as it is one of the most expensive inner loops.

The computation of squared root at the end is omitted for efficiency.

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The documentation for this struct was generated from the following file:

• dist.h