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BinaryDescriptor Class implements both functionalities for detection of lines and computation of their binary descriptor. Class' interface is mainly based on the ones of classical detectors and extractors, such as Feature2d's `FeatureDetector <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html?highlight=featuredetector#featuredetector>`_ and `DescriptorExtractor <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_extractors.html?highlight=extractor#DescriptorExtractor : public Algorithm>`_.
Retrieved information about lines is stored in *KeyLine* objects.
If no argument is provided, constructor sets default values (see comments in the code snippet in previous section). Default values are strongly reccomended.
BinaryDescriptor::getNumOfOctaves
---------------------------------
Get current number of octaves
.. ocv:function:: int BinaryDescriptor::getNumOfOctaves()
BinaryDescriptor::setNumOfOctaves
---------------------------------
Set number of octaves
.. ocv:function:: void BinaryDescriptor::setNumOfOctaves( int octaves )
:param octaves: number of octaves
BinaryDescriptor::getWidthOfBand
--------------------------------
Get current width of bands
.. ocv:function:: int BinaryDescriptor::getWidthOfBand()
BinaryDescriptor::setWidthOfBand
--------------------------------
Set width of bands
.. ocv:function:: void BinaryDescriptor::setWidthOfBand( int width )
:param width: width of bands
BinaryDescriptor::getReductionRatio
-----------------------------------
Get current reduction ratio (used in Gaussian pyramids)
.. ocv:function:: int BinaryDescriptor::getReductionRatio()
BinaryDescriptor::setReductionRatio
-----------------------------------
Set reduction ratio (used in Gaussian pyramids)
.. ocv:function:: void BinaryDescriptor::setReductionRatio( int rRatio )
:param rRatio: reduction ratio
BinaryDescriptor::createBinaryDescriptor
----------------------------------------
Create a BinaryDescriptor object with default parameters (or with the ones provided) and return a smart pointer to it
:param mask: mask matrix to select which lines in KeyLines must be accepted among the ones extracted (used when *keylines* is not empty)
:param keylines: vector that contains input lines (when filled, the detection part will be skipped and input lines will be passed as input to the algorithm computing descriptors)
:param descriptors: matrix that will store final descriptors
:param useProvidedKeyLines: flag (when set to true, detection phase will be skipped and only computation of descriptors will be executed, using lines provided in *keylines*)
BinaryDescriptor::read
----------------------
Read parameters from a FileNode object and store them
BinaryDescriptor Class implements both functionalities for detection of lines and computation of their binary descriptor. Class' interface is mainly based on the ones of classical detectors and extractors, such as Feature2d's `FeatureDetector <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html?highlight=featuredetector#featuredetector>`_ and `DescriptorExtractor <http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_extractors.html?highlight=extractor#DescriptorExtractor : public Algorithm>`_.
Retrieved information about lines is stored in *KeyLine* objects.
If no argument is provided, constructor sets default values (see comments in the code snippet in previous section). Default values are strongly reccomended.
BinaryDescriptor::getNumOfOctaves
---------------------------------
Get current number of octaves
.. ocv:function:: int BinaryDescriptor::getNumOfOctaves()
BinaryDescriptor::setNumOfOctaves
---------------------------------
Set number of octaves
.. ocv:function:: void BinaryDescriptor::setNumOfOctaves( int octaves )
:param octaves: number of octaves
BinaryDescriptor::getWidthOfBand
--------------------------------
Get current width of bands
.. ocv:function:: int BinaryDescriptor::getWidthOfBand()
BinaryDescriptor::setWidthOfBand
--------------------------------
Set width of bands
.. ocv:function:: void BinaryDescriptor::setWidthOfBand( int width )
:param width: width of bands
BinaryDescriptor::getReductionRatio
-----------------------------------
Get current reduction ratio (used in Gaussian pyramids)
.. ocv:function:: int BinaryDescriptor::getReductionRatio()
BinaryDescriptor::setReductionRatio
-----------------------------------
Set reduction ratio (used in Gaussian pyramids)
.. ocv:function:: void BinaryDescriptor::setReductionRatio( int rRatio )
:param rRatio: reduction ratio
BinaryDescriptor::createBinaryDescriptor
----------------------------------------
Create a BinaryDescriptor object with default parameters (or with the ones provided) and return a smart pointer to it
:param mask: mask matrix to select which lines in KeyLines must be accepted among the ones extracted (used when *keylines* is not empty)
:param keylines: vector that contains input lines (when filled, the detection part will be skipped and input lines will be passed as input to the algorithm computing descriptors)
:param descriptors: matrix that will store final descriptors
:param useProvidedKeyLines: flag (when set to true, detection phase will be skipped and only computation of descriptors will be executed, using lines provided in *keylines*)
BinaryDescriptor::read
----------------------
Read parameters from a FileNode object and store them
The lines extraction methodology described in the following is mainly based on [LBD]_.
The extraction starts with a Gaussian pyramid generated from an original image, downsampled and blurred N-1 times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm.
The extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm.
Differently from EDLine lines extractor used in original article, LSD furnishes information only about lines extremes; thus, additional information regarding slope and equation of line are computed via analytic methods. The number of pixels is obtained using `LineIterator <http://docs.opencv.org/modules/core/doc/drawing_functions.html#lineiterator>`_. Later on, all extracted lines are arranged in buckets: two lines fall in the same bucket if they represent the same line in different octave (they have the same direction and belong to same region of original image). The set of buckets becomes the input for descriptors computation.
...
...
@@ -125,6 +125,14 @@ Each :math:`BD_j` can be obtained using the standard deviation vector :math:`S_j
Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary representation of a single LBD.
Related Pages
-------------
* `BinaryDescriptor Class <BinaryDescriptor.html>`_
The lines extraction methodology described in the following is mainly based on [LBD]_.
The extraction starts with a Gaussian pyramid generated from an original image, downsampled and blurred N-1 times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm.
The extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm.
Differently from EDLine lines extractor used in original article, LSD furnishes information only about lines extremes; thus, additional information regarding slope and equation of line are computed via analytic methods. The number of pixels is obtained using `LineIterator <http://docs.opencv.org/modules/core/doc/drawing_functions.html#lineiterator>`_. Later on, all extracted lines are arranged in buckets: two lines fall in the same bucket if they represent the same line in different octave (they have the same direction and belong to same region of original image). The set of buckets becomes the input for descriptors computation.
...
...
@@ -123,3 +123,17 @@ Each :math:`BD_j` can be obtained using the standard deviation vector :math:`S_j
Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary representation of a single LBD.
Related Pages
-------------
* `BinaryDescriptor <BinaryDescriptor.html>`
References
----------
.. [LBD] Zhang, Lilian, and Reinhard Koch. *An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency*, Journal of Visual Communication and Image Representation 24.7 (2013): 794-805.
