@ -9,10 +9,10 @@ FAB-MAP is an approach to appearance-based place recognition. FAB-MAP compares i
openFABMAP requires training data (e.g. a collection of images from a similar but not identical environment) to construct a visual vocabulary for the visual bag-of-words model, along with a Chow-Liu tree representation of feature likelihood and for use in the Sampled new place method (see below).
FabMap
of2::FabMap
--------------------
..ocv:class:: FabMap
..ocv:class::of2::FabMap
The main FabMap class performs the comparison between visual bags-of-words extracted from one or more images. The FabMap class is instantiated as one of the four inherited FabMap classes (FabMap1, FabMapLUT, FabMapFBO, FabMap2). Each inherited class performs the comparison differently based on algorithm iterations as published (see each class below for specifics). A Chow-Liu tree, detector model parameters and some option flags are common to all Fabmap variants and are supplied on class creation. Training data (visual bag-of-words) is supplied to the class if using the SAMPLED new place method. Test data (visual bag-of-words) is supplied as images to which query bag-of-words are compared against. The common flags are listed below: ::
@ -149,10 +149,10 @@ The inverted index FAB-MAP as in [IJRR2010]_. This version of FAB-MAP is the fas
..[ICRA2011] A. Glover, et al., "OpenFABMAP: An Open Source Toolbox for Appearance-based Loop Closure Detection," in IEEE International Conference on Robotics and Automation, St Paul, Minnesota, 2011
ImageMatch
of2::IMatch
--------------------
..ocv:struct:: IMatch
..ocv:struct::of2::IMatch
FAB-MAP comparison results are stored in a vector of IMatch structs. Each IMatch structure provides the index of the provided query bag-of-words, the index of the test bag-of-words, the raw log-likelihood of the match (independent of other comparisons), and the match probability (normalised over other comparison likelihoods).
@ -180,48 +180,48 @@ FAB-MAP comparison results are stored in a vector of IMatch structs. Each IMatch
};
Chow-LiuTree
of2::ChowLiuTree
--------------------
..ocv:class:: ChowLiuTree
..ocv:class::of2::ChowLiuTree
The Chow-Liu tree is a probabilistic model of the environment in terms of feature occurance and co-occurance. The Chow-Liu tree is a form of Bayesian network. FAB-MAP uses the model when calculating bag-of-words similarity by taking into account feature saliency. Training data is provided to the ChowLiuTree class in the form of bag-of-words image descriptors. The make function produces a cv::Mat that encodes the tree structure.
Returns a vector containing multiple bag-of-words image descriptors
..ocv:function:: Mat make(double infoThreshold = 0.0)
..ocv:function:: Mat of2::ChowLiuTree::make(double infoThreshold = 0.0)
:param infoThreshold:a threshold can be set to reduce the amount of memory used when making the Chow-Liu tree, which can occur with large vocabulary sizes. This function can fail if the threshold is set too high. If memory is an issue the value must be set by trial and error (~0.0005)
BOWMSCTrainer
of2::BOWMSCTrainer
--------------------
..ocv:class:: BOWMSCTrainer : public BOWTrainer
..ocv:class::of2::BOWMSCTrainer : public of2::BOWTrainer
BOWMSCTrainer is a custom clustering algorithm used to produce the feature vocabulary required to create bag-of-words representations. The algorithm is an implementation of [AVC2007]_. Arguments against using K-means for the FAB-MAP algorithm are discussed in [IJRR2010]_. The BOWMSCTrainer inherits from the cv::BOWTrainer class, overwriting the cluster function.
Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
..ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
..ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
:param src:Input 8-bit 1-channel, 2-channel or 3-channel image.
Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/
with several computational optimizations. Noise expected to be a gaussian white noise
..ocv:function:: void fastNlMeansDenoising( Mat& src, Mat& dst, int templateWindowSize, int searchWindowSize, int h )
..ocv:function:: void fastNlMeansDenoising( InputArray src, OutputArray dst, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
:param src:Input 8-bit 1-channel, 2-channel or 3-channel image.
@ -25,10 +25,10 @@ Advanced usage of this functions can be manual denoising of colored image in dif
Such approach is used in ``fastNlMeansDenoisingColored`` by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.
fastNlMeansDenoisingColored
-----------
---------------------------
Modification of ``fastNlMeansDenoising`` function for colored images
..ocv:function:: void fastNlMeansDenoisingColored( Mat& src, Mat& dst, int templateWindowSize, int searchWindowSize, int h, int hForColorComponents )
..ocv:function:: void fastNlMeansDenoisingColored( InputArray src, OutputArray dst, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
:param src:Input 8-bit 3-channel image.
@ -45,11 +45,11 @@ Modification of ``fastNlMeansDenoising`` function for colored images
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``fastNlMeansDenoising`` function.
fastNlMeansDenoisingMulti
-----------
-------------------------
Modification of ``fastNlMeansDenoising`` function for images sequence where consequtive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces.
For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394
..ocv:function:: void fastNlMeansDenoisingMulti( const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize, Mat& dst, int templateWindowSize, int searchWindowSize, int h)
..ocv:function:: void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, int templateWindowSize=7, int searchWindowSize=21 )
:param srcImgs:Input 8-bit 1-channel, 2-channel or 3-channel images sequence. All images should have the same type and size.
@ -66,10 +66,10 @@ For more details see http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131
:param h:Parameter regulating filter strength for luminance component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
fastNlMeansDenoisingColoredMulti
-----------
--------------------------------
Modification of ``fastNlMeansDenoisingMulti`` function for colored images sequences
..ocv:function:: void fastNlMeansDenoisingColoredMulti( const std::vector<Mat>& srcImgs, int imgToDenoiseIndex, int temporalWindowSize, Mat& dst, int templateWindowSize, int searchWindowSize, int h, int hForColorComponents)
..ocv:function:: void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h=3, float hColor=3, int templateWindowSize=7, int searchWindowSize=21 )
:param srcImgs:Input 8-bit 3-channel images sequence. All images should have the same type and size.
Template method that runs the Fast Marching Method.
..ocv:function:: Inpaint FastMarchingMethod::run(const Mat &mask, Inpaint inpaint)
..ocv:function::template<typename Inpaint> Inpaint videostab::FastMarchingMethod::run(const Mat &mask, Inpaint inpaint)
:param mask:Image mask. ``0`` value indicates that the pixel value must be inpainted, ``255`` indicates that the pixel value is known, other values aren't acceptable.
:param model:Motion model. See :ocv:class:`videostab::MotionModel`.
@ -101,7 +99,7 @@ Estimates best global motion between two 2D point clouds in the least-squares se
..note:: Works in-place and changes input point arrays.
..ocv:function:: Mat estimateGlobalMotionLeastSquares(InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE, float *rmse = 0)
..ocv:function:: Mat videostab::estimateGlobalMotionLeastSquares(InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE, float *rmse = 0)
Andrey Kamaev
12 years ago