examples/OpenCV/siftDetector/hess/kdtree.h File Reference#include "cxcore.h"
Go to the source code of this file.
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Classes |
struct | kd_node |
Functions |
struct kd_node * | kdtree_build (struct feature *features, int n) |
int | kdtree_bbf_knn (struct kd_node *kd_root, struct feature *feat, int k, struct feature ***nbrs, int max_nn_chks) |
int | kdtree_bbf_spatial_knn (struct kd_node *kd_root, struct feature *feat, int k, struct feature ***nbrs, int max_nn_chks, CvRect rect, int model) |
void | kdtree_release (struct kd_node *kd_root) |
Detailed Description
Functions and structures for maintaining a k-d tree database of image features.
For more information, refer to:
Beis, J. S. and Lowe, D. G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In Conference on Computer Vision and Pattern Recognition (CVPR) (2003), pp. 1000--1006.
Copyright (C) 2006-2007 Rob Hess <hess@eecs.oregonstate.edu>
- Version:
- 1.1.1-20070913
Definition in file kdtree.h.
Function Documentation
int kdtree_bbf_knn |
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struct kd_node * |
kd_root, |
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struct feature * |
feat, |
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int |
k, |
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struct feature *** |
nbrs, |
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int |
max_nn_chks | |
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Finds an image feature's approximate k nearest neighbors in a kd tree using Best Bin First search.
- Parameters:
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| kd_root | root of an image feature kd tree |
| feat | image feature for whose neighbors to search |
| k | number of neighbors to find |
| nbrs | pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance |
| max_nn_chks | search is cut off after examining this many tree entries |
- Returns:
- Returns the number of neighbors found and stored in nbrs, or -1 on error.
Definition at line 93 of file kdtree.cpp.
int kdtree_bbf_spatial_knn |
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struct kd_node * |
kd_root, |
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struct feature * |
feat, |
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int |
k, |
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struct feature *** |
nbrs, |
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int |
max_nn_chks, |
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CvRect |
rect, |
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int |
model | |
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Finds an image feature's approximate k nearest neighbors within a specified spatial region in a kd tree using Best Bin First search.
- Parameters:
-
| kd_root | root of an image feature kd tree |
| feat | image feature for whose neighbors to search |
| k | number of neighbors to find |
| nbrs | pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance |
| max_nn_chks | search is cut off after examining this many tree entries |
| rect | rectangular region in which to search for neighbors |
| model | if true, spatial search is based on kdtree features' model locations; otherwise it is based on their image locations |
- Returns:
- Returns the number of neighbors found and stored in nbrs (in case k neighbors could not be found before examining max_nn_checks keypoint entries).
Definition at line 191 of file kdtree.cpp.
struct kd_node* kdtree_build |
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struct feature * |
features, |
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int |
n | |
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A function to build a k-d tree database from keypoints in an array.
- Parameters:
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| features | an array of features |
| n | the number of features in features |
- Returns:
- Returns the root of a kd tree built from features.
Definition at line 60 of file kdtree.cpp.
void kdtree_release |
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struct kd_node * |
kd_root |
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De-allocates memory held by a kd tree
- Parameters:
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| kd_root | pointer to the root of a kd tree |
Definition at line 228 of file kdtree.cpp.
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