@ -248,7 +248,7 @@ Creates a descriptor matcher of a given type with the default parameters (using
*
*
``BruteForce-Hamming``
``BruteForce-Hamming``
*
*
``BruteForce-HammingLUT``
``BruteForce-Hamming(2)``
*
*
``FlannBased``
``FlannBased``
@ -256,102 +256,22 @@ Creates a descriptor matcher of a given type with the default parameters (using
BruteForceMatcher
BFMatcher
-----------------
-----------------
..ocv:class::BruteForceMatcher
..ocv:class::BFMatcher
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets. ::
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets. ::
template<class Distance>
class BruteForceMatcher : public DescriptorMatcher
For efficiency, ``BruteForceMatcher`` is used as a template parameterized with the distance type. For float descriptors, ``L2<float>`` is a common choice. The following distances are supported: ::
..ocv:function:: BFMatcher::BFMatcher( int distanceType, bool crossCheck=false )
template<typename T>
:param distanceType:One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
struct Accumulator
{
typedef T Type;
};
template<> struct Accumulator<unsigned char> { typedef unsigned int Type; };
template<> struct Accumulator<unsigned short> { typedef unsigned int Type; };
template<> struct Accumulator<char> { typedef int Type; };
template<> struct Accumulator<short> { typedef int Type; };
/*
* Euclidean distance functor
*/
template<class T>
struct L2
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const;
};
/*
:param crossCheck:If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMathcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
* Squared Euclidean distance functor
*/
template<class T>
struct SL2
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const;
};
// Note: in case of SL2 distance a parameter maxDistance in the method DescriptorMatcher::radiusMatch
:param image:Image where keypoints (corners) are detected.
:param image:Image where keypoints (corners) are detected.
@ -17,7 +17,9 @@ Detects corners using the FAST algorithm
:param nonmaxSupression:If it is true, non-maximum suppression is applied to detected corners (keypoints).
:param nonmaxSupression:If it is true, non-maximum suppression is applied to detected corners (keypoints).
Detects corners using the FAST algorithm by E. Rosten (*Machine Learning for High-speed Corner Detection*, 2006).
Detects corners using the FAST algorithm by [Rosten06]_.
..[Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006.
MSER
MSER
@ -46,102 +48,51 @@ The class encapsulates all the parameters of the MSER extraction algorithm (see
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for useful comments and parameters description.
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for useful comments and parameters description.
StarDetector
ORB
------------
---
..ocv:class:: StarDetector
..ocv:class:: ORB
Class implementing the ``Star`` keypoint detector, a modified version of the ``CenSurE`` keypoint detector described in [Agrawal08]_.
..[Agrawal08] Agrawal, M. and Konolige, K. and Blas, M.R. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching", ECCV08, 2008
Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
StarDetector::StarDetector
..[RRKB11] Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to SIFT or SURF. ICCV 2011: 2564-2571.
--------------------------
The Star Detector constructor
..ocv:function:: StarDetector::StarDetector()
ORB::ORB
--------
The ORB constructor
..ocv:function:: StarDetector::StarDetector(int maxSize, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize)
..ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31)
:param maxSize:maximum size of the features. The following values are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128. In the case of a different value the result is undefined.
:param nfeatures:The maximum number of features to retain.
:param responseThreshold:threshold for the approximated laplacian, used to eliminate weak features. The larger it is, the less features will be retrieved
:param scaleFactor:Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
:param lineThresholdProjected:another threshold for the laplacian to eliminate edges
:param nlevels:The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
:param lineThresholdBinarized:yet another threshold for the feature size to eliminate edges. The larger the 2nd threshold, the more points you get.
:param edgeThreshold:This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
:param firstLevel:It should be 0 in the current implementation.
:param WTA_K:The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
:param scoreType:The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
:param patchSize:size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
StarDetector::operator()
ORB::operator()
------------------------
---------------
Finds keypoints in an image
Finds keypoints in an image and computes their descriptors
..[Lowe04] Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004.
SIFT::SIFT
----------
The SIFT constructors.
..ocv:function:: SIFT::SIFT( int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6)
:param nfeatures:The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
:param nOctaveLayers:The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
:param contrastThreshold:The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
:param edgeThreshold:The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the ``edgeThreshold``, the less features are filtered out (more features are retained).
:param sigma:The sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
SIFT::operator ()
-----------------
Extract features and computes their descriptors using SIFT algorithm
:param mask:Optional input mask that marks the regions where we should detect features.
:param keypoints:The input/output vector of keypoints
:param descriptors:The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them.
:param useProvidedKeypoints:Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
SURF
SURF
----
----
..ocv:class:: SURF
..ocv:class:: SURF
@ -146,8 +101,8 @@ SURF::operator()
----------------
----------------
Detects keypoints and computes SURF descriptors for them.
Detects keypoints and computes SURF descriptors for them.
@ -162,7 +117,7 @@ Detects keypoints and computes SURF descriptors for them.
:param keypoints:The input/output vector of keypoints
:param keypoints:The input/output vector of keypoints
:param descriptors:The output concatenated vectors of descriptors. Each descriptor is 64- or 128-element vector, as returned by ``SURF::descriptorSize()``. So the total size of ``descriptors`` will be ``keypoints.size()*descriptorSize()``.
:param descriptors:The output matrix of descriptors. Pass ``cv::noArray()`` if you do not need them.
:param useProvidedKeypoints:Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
:param useProvidedKeypoints:Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
Vadim Pisarevsky
13 years ago