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removed the stuff that's now in xfeatures2d; temporarily added dummy definition of SIFT to make doc builder pass (will remove it later)

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Vadim Pisarevsky 2014-08-12 00:29:56 +04:00
parent 27d2d3cbac
commit fe7b48aa8f
3 changed files with 10 additions and 71 deletions
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78
modules/features2d/doc/feature_detection_and_description.rst
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@ -36,38 +36,6 @@ Detects corners using the FAST algorithm by [Rosten06]_.
.. [Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006. .. [Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006.
BriefDescriptorExtractor
------------------------
.. ocv:class:: BriefDescriptorExtractor : public DescriptorExtractor
Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V.,
Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* ,
11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer, September 2010. ::
class BriefDescriptorExtractor : public DescriptorExtractor
{
public:
static const int PATCH_SIZE = 48;
static const int KERNEL_SIZE = 9;
// bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.
BriefDescriptorExtractor( int bytes = 32 );
virtual void read( const FileNode& );
virtual void write( FileStorage& ) const;
virtual int descriptorSize() const;
virtual int descriptorType() const;
virtual int defaultNorm() const;
protected:
...
};
.. note::
* A complete BRIEF extractor sample can be found at opencv_source_code/samples/cpp/brief_match_test.cpp
MSER MSER
---- ----
.. ocv:class:: MSER : public FeatureDetector .. ocv:class:: MSER : public FeatureDetector
@ -213,43 +181,6 @@ Finds keypoints in an image and computes their descriptors
:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them. :param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
FREAK
-----
.. ocv:class:: FREAK : public DescriptorExtractor
Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in [AOV12]_. The algorithm propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are competitive alternatives to existing keypoints in particular for embedded applications.
.. [AOV12] A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award Winner.
.. note::
* An example on how to use the FREAK descriptor can be found at opencv_source_code/samples/cpp/freak_demo.cpp
FREAK::FREAK
------------
The FREAK constructor
.. ocv:function:: FREAK::FREAK( bool orientationNormalized=true, bool scaleNormalized=true, float patternScale=22.0f, int nOctaves=4, const vector<int>& selectedPairs=vector<int>() )
:param orientationNormalized: Enable orientation normalization.
:param scaleNormalized: Enable scale normalization.
:param patternScale: Scaling of the description pattern.
:param nOctaves: Number of octaves covered by the detected keypoints.
:param selectedPairs: (Optional) user defined selected pairs indexes,
FREAK::selectPairs
------------------
Select the 512 best description pair indexes from an input (grayscale) image set. FREAK is available with a set of pairs learned off-line. Researchers can run a training process to learn their own set of pair. For more details read section 4.2 in: A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.
We notice that for keypoint matching applications, image content has little effect on the selected pairs unless very specific what does matter is the detector type (blobs, corners,...) and the options used (scale/rotation invariance,...). Reduce corrThresh if not enough pairs are selected (43 points --> 903 possible pairs)
.. ocv:function:: vector<int> FREAK::selectPairs(const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const double corrThresh = 0.7, bool verbose = true)
:param images: Grayscale image input set.
:param keypoints: Set of detected keypoints
:param corrThresh: Correlation threshold.
:param verbose: Prints pair selection informations.
KAZE KAZE
---- ----
.. ocv:class:: KAZE : public Feature2D .. ocv:class:: KAZE : public Feature2D
@ -312,3 +243,12 @@ The AKAZE constructor
:param octaves: Maximum octave evolution of the image :param octaves: Maximum octave evolution of the image
:param sublevels: Default number of sublevels per scale level :param sublevels: Default number of sublevels per scale level
:param diffusivity: Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER :param diffusivity: Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER
SIFT
----
.. ocv:class:: SIFT : public Feature2D
The SIFT algorithm has been moved to opencv_contrib/xfeatures2d module.

1
modules/features2d/doc/features2d.rst
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@ -11,6 +11,5 @@ features2d. 2D Features Framework
common_interfaces_of_feature_detectors common_interfaces_of_feature_detectors
common_interfaces_of_descriptor_extractors common_interfaces_of_descriptor_extractors
common_interfaces_of_descriptor_matchers common_interfaces_of_descriptor_matchers
common_interfaces_of_generic_descriptor_matchers
drawing_function_of_keypoints_and_matches drawing_function_of_keypoints_and_matches
object_categorization object_categorization