<?xml version="1.0"?>
<doc>
    <assembly>
        <name>Emgu.CV.Contrib</name>
    </assembly>
    <members>
        <member name="T:Emgu.CV.XFeatures2D.BriefDescriptorExtractor">
            <summary>
            BRIEF Descriptor
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.BriefDescriptorExtractor.#ctor(System.Int32)">
            <summary>
            Create a BRIEF descriptor extractor.
            </summary>
            <param name="descriptorSize">The size of descriptor. It can be equal 16, 32 or 64 bytes.</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.BriefDescriptorExtractor.DisposeObject">
            <summary>
            Release all the unmanaged resource associated with BRIEF
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.ContribInvoke">
            <summary>
            This class wraps the functional calls to the opencv contrib modules
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.CudaSURF">
            <summary>
            A SURF detector using Cuda
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.#ctor(System.Single,System.Int32,System.Int32,System.Boolean,System.Single,System.Boolean)">
            <summary>
            Create a Cuda SURF detector
            </summary>
            <param name="hessianThreshold">The interest operator threshold.</param>
            <param name="nOctaves">The number of octaves to process.</param>
            <param name="nOctaveLayers">The number of layers in each octave.</param>
            <param name="extended">True, if generate 128-len descriptors, false - 64-len descriptors.</param>
            <param name="featuresRatio">Max features = featuresRatio * img.size().srea().</param>
            <param name="upright">If set to true, the orientation is not computed for the keypoints</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.DetectKeyPointsRaw(Emgu.CV.Cuda.GpuMat,Emgu.CV.Cuda.GpuMat)">
            <summary>
            Detect keypoints in the CudaImage
            </summary>
            <param name="img">The image where keypoints will be detected from</param>
            <param name="mask">The optional mask, can be null if not needed</param>
            <returns>
            The keypoints GpuMat that will have 1 row.
            keypoints.at&lt;float[6]&gt;(1, i) contains i'th keypoint
            format: (x, y, size, response, angle, octave)
            </returns>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.DetectKeyPoints(Emgu.CV.Cuda.GpuMat,Emgu.CV.Cuda.GpuMat)">
            <summary>
            Detect keypoints in the CudaImage
            </summary>
            <param name="img">The image where keypoints will be detected from</param>
            <param name="mask">The optional mask, can be null if not needed</param>
            <returns>An array of keypoints</returns>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.DownloadKeypoints(Emgu.CV.Cuda.GpuMat,Emgu.CV.Util.VectorOfKeyPoint)">
            <summary>
            Obtain the keypoints array from GpuMat
            </summary>
            <param name="src">The keypoints obtained from DetectKeyPointsRaw</param>
            <param name="dst">The vector of keypoints</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.UploadKeypoints(Emgu.CV.Util.VectorOfKeyPoint,Emgu.CV.Cuda.GpuMat)">
            <summary>
            Obtain a GpuMat from the keypoints array
            </summary>
            <param name="src">The keypoints array</param>
            <param name="dst">A GpuMat that represent the keypoints</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.ComputeDescriptorsRaw(Emgu.CV.Cuda.GpuMat,Emgu.CV.Cuda.GpuMat,Emgu.CV.Cuda.GpuMat)">
            <summary>
            Compute the descriptor given the image and the point location
            </summary>
            <param name="image">The image where the descriptor will be computed from</param>
            <param name="mask">The optional mask, can be null if not needed</param>
            <param name="keyPoints">The keypoint where the descriptor will be computed from. The order of the keypoints might be changed unless the GPU_SURF detector is UP-RIGHT.</param>
            <returns>The image features founded on the keypoint location</returns>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.CudaSURF.DisposeObject">
            <summary>
            Release the unmanaged resource associate to the Detector
            </summary>
        </member>
        <member name="P:Emgu.CV.XFeatures2D.CudaSURF.DescriptorSize">
            <summary>
            Return the size of the descriptor (64/128)
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.DAISY">
            <summary>
            Daisy descriptor.
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.DAISY.#ctor(System.Single,System.Int32,System.Int32,System.Int32,Emgu.CV.XFeatures2D.DAISY.NormalizationType,Emgu.CV.IInputArray,System.Boolean,System.Boolean)">
            <summary>
            Create DAISY descriptor extractor
            </summary>
            <param name="radius">Radius of the descriptor at the initial scale.</param>
            <param name="qRadius">Amount of radial range division quantity.</param>
            <param name="qTheta">Amount of angular range division quantity.</param>
            <param name="qHist">Amount of gradient orientations range division quantity.</param>
            <param name="norm">Descriptors normalization type.</param>
            <param name="H">optional 3x3 homography matrix used to warp the grid of daisy but sampling keypoints remains unwarped on image</param>
            <param name="interpolation">Switch to disable interpolation for speed improvement at minor quality loss</param>
            <param name="useOrientation">Sample patterns using keypoints orientation, disabled by default.</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.DAISY.DisposeObject">
            <summary>
            Release all the unmanaged resource associated with BRIEF
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.DAISY.NormalizationType">
            <summary>
            Normalization type
            </summary>
        </member>
        <member name="F:Emgu.CV.XFeatures2D.DAISY.NormalizationType.None">
            <summary>
            Will not do any normalization (default)
            </summary>
        </member>
        <member name="F:Emgu.CV.XFeatures2D.DAISY.NormalizationType.Partial">
            <summary>
            Histograms are normalized independently for L2 norm equal to 1.0
            </summary>
        </member>
        <member name="F:Emgu.CV.XFeatures2D.DAISY.NormalizationType.Full">
            <summary>
            Descriptors are normalized for L2 norm equal to 1.0
            </summary>
        </member>
        <member name="F:Emgu.CV.XFeatures2D.DAISY.NormalizationType.SIFT">
            <summary>
            Descriptors are normalized for L2 norm equal to 1.0 but no individual one is bigger than 0.154 as in SIFT
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.Freak">
            <summary>
            The FREAK (Fast Retina Keypoint) keypoint descriptor:
            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.
            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.
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.Freak.#ctor(System.Boolean,System.Boolean,System.Single,System.Int32)">
            <summary>
            Create a Freak descriptor extractor.
            </summary>
            <param name="orientationNormalized">Enable orientation normalization</param>
            <param name="scaleNormalized">Enable scale normalization</param>
            <param name="patternScale">Scaling of the description pattern</param>
            <param name="nOctaves">Number of octaves covered by the detected keypoints.</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.Freak.DisposeObject">
            <summary>
            Release all the unmanaged resource associated with BRIEF
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.LATCH">
            <summary>
            latch Class for computing the LATCH descriptor.
            If you find this code useful, please add a reference to the following paper in your work:
            Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015
            LATCH is a binary descriptor based on learned comparisons of triplets of image patches.
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.LATCH.#ctor(System.Int32,System.Boolean,System.Int32)">
            <summary>
            Create LATCH descriptor extractor
            </summary>
            <param name="bytes">The size of the descriptor - can be 64, 32, 16, 8, 4, 2 or 1</param>
            <param name="rotationInvariance">Whether or not the descriptor should compensate for orientation changes.</param>
            <param name="halfSsdSize">the size of half of the mini-patches size. For example, if we would like to compare triplets of patches of size 7x7x
            then the half_ssd_size should be (7-1)/2 = 3.</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.LATCH.DisposeObject">
            <summary>
            Release all the unmanaged resource associated with BRIEF
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.LUCID">
            <summary>
            The locally uniform comparison image descriptor:
            An image descriptor that can be computed very fast, while being
            about as robust as, for example, SURF or BRIEF.
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.LUCID.#ctor(System.Int32,System.Int32)">
            <summary>
            Create a locally uniform comparison image descriptor.
            </summary>
            <param name="lucidKernel">Kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth</param>
            <param name="blurKernel">kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.LUCID.DisposeObject">
            <summary>
            Release all the unmanaged resource associated with BRIEF
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.SIFT">
            <summary>
            Wrapped SIFT detector
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.SIFT.#ctor(System.Int32,System.Int32,System.Double,System.Double,System.Double)">
            <summary>
            Create a SIFT using the specific values
            </summary>
            <param name="nFeatures">The desired number of features. Use 0 for un-restricted number of features</param>
            <param name="nOctaveLayers">The number of octave layers. Use 3 for default</param>
            <param name="contrastThreshold">Contrast threshold. Use 0.04 as default</param>
            <param name="edgeThreshold">Detector parameter. Use 10.0 as default</param>
            <param name="sigma">Use 1.6 as default</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.SIFT.DisposeObject">
            <summary>
            Release the unmanaged resources associated with this object
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.StarDetector">
            <summary>
            StarDetector
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.StarDetector.#ctor(System.Int32,System.Int32,System.Int32,System.Int32,System.Int32)">
             <summary>
             Create a star detector with the specific parameters
             </summary>
             <param name="maxSize">
             Maximum size of the features. The following
             values of the parameter are supported:
             4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128</param>
             <param name="responseThreshold">
             Threshold for the approximated laplacian,
             used to eliminate weak features. The larger it is,
             the less features will be retrieved
             </param>
             <param name="lineThresholdProjected">
             Another threshold for the laplacian to eliminate edges.
             The larger the threshold, the more points you get.
             </param>
             <param name="lineThresholdBinarized">
             Another threshold for the feature size to eliminate edges. 
             The larger the threshold, the more points you get.</param>
             <param name="suppressNonmaxSize">
            
             </param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.StarDetector.DisposeObject">
            <summary>
            Release the unmanaged memory associated with this detector.
            </summary>
        </member>
        <member name="T:Emgu.CV.XFeatures2D.SURF">
            <summary>
            Class for extracting Speeded Up Robust Features from an image
            </summary>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.SURF.#ctor(System.Double,System.Int32,System.Int32,System.Boolean,System.Boolean)">
            <summary>
            Create a SURF detector using the specific values
            </summary>
            <param name="hessianThresh">      
            Only features with keypoint.hessian larger than that are extracted.
            good default value is ~300-500 (can depend on the average local contrast and sharpness of the image).
            user can further filter out some features based on their hessian values and other characteristics
            </param>
            <param name="extended">      
            false means basic descriptors (64 elements each),
            true means extended descriptors (128 elements each)
            </param>
            <param name="nOctaves">
            The number of octaves to be used for extraction.
            With each next octave the feature size is doubled
            </param>
            <param name="nOctaveLayers">
            The number of layers within each octave
            </param>
            <param name="upright">
            False means that detector computes orientation of each feature. 
            True means that the orientation is not computed (which is much, much faster). 
            For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting upright=true.</param>
        </member>
        <member name="M:Emgu.CV.XFeatures2D.SURF.DisposeObject">
            <summary>
            Release the unmanaged memory associated with this detector.
            </summary>
        </member>
        <member name="T:Emgu.CV.Face.FaceRecognizer">
            <summary>
            Face Recognizer
            </summary>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.Train(Emgu.CV.IInputArray,Emgu.CV.IInputArray)">
            <summary>
            Train the face recognizer with the specific images and labels
            </summary>
            <param name="images">The images used in the training. This can be a VectorOfMat</param>
            <param name="labels">The labels of the images. This can be a VectorOfInt</param>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.Train``2(Emgu.CV.Image{``0,``1}[],System.Int32[])">
            <summary>
            Train the face recognizer with the specific images and labels
            </summary>
            <param name="images">The images used in the training.</param>
            <param name="labels">The labels of the images.</param>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.Predict(Emgu.CV.IInputArray)">
            <summary>
            Predict the label of the image
            </summary>
            <param name="image">The image where prediction will be based on</param>
            <returns>The prediction label</returns>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.Save(System.String)">
            <summary>
            Save the FaceRecognizer to a file
            </summary>
            <param name="fileName">The file name to be saved to</param>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.Load(System.String)">
            <summary>
            Load the FaceRecognizer from the file
            </summary>
            <param name="fileName">The file where the FaceRecognizer will be loaded from</param>
        </member>
        <member name="M:Emgu.CV.Face.FaceRecognizer.DisposeObject">
            <summary>
            Release the unmanaged memory associated with this FaceRecognizer
            </summary>
        </member>
        <member name="T:Emgu.CV.Face.FaceRecognizer.PredictionResult">
            <summary>
            The prediction result
            </summary>
        </member>
        <member name="F:Emgu.CV.Face.FaceRecognizer.PredictionResult.Label">
            <summary>
            The label
            </summary>
        </member>
        <member name="F:Emgu.CV.Face.FaceRecognizer.PredictionResult.Distance">
            <summary>
            The distance
            </summary>
        </member>
        <member name="T:Emgu.CV.Face.EigenFaceRecognizer">
            <summary>
            Eigen face recognizer
            </summary>
        </member>
        <member name="M:Emgu.CV.Face.EigenFaceRecognizer.#ctor(System.Int32,System.Double)">
            <summary>
            Create an EigenFaceRecognizer
            </summary>
            <param name="numComponents">The number of components</param>
            <param name="threshold">The distance threshold</param>
        </member>
        <member name="T:Emgu.CV.Face.FisherFaceRecognizer">
            <summary>
            Fisher face recognizer
            </summary>
        </member>
        <member name="M:Emgu.CV.Face.FisherFaceRecognizer.#ctor(System.Int32,System.Double)">
            <summary>
            Create a FisherFaceRecognizer
            </summary>
            <param name="numComponents">The number of components</param>
            <param name="threshold">The distance threshold</param>
        </member>
        <member name="T:Emgu.CV.Face.LBPHFaceRecognizer">
            <summary>
            LBPH face recognizer
            </summary>
        </member>
        <member name="M:Emgu.CV.Face.LBPHFaceRecognizer.#ctor(System.Int32,System.Int32,System.Int32,System.Int32,System.Double)">
            <summary>
            Create a LBPH face recognizer
            </summary>
            <param name="radius">Radius</param>
            <param name="neighbors">Neighbors</param>
            <param name="gridX">Grid X</param>
            <param name="gridY">Grid Y</param>
            <param name="threshold">The distance threshold</param>
        </member>
        <member name="M:Emgu.CV.Face.LBPHFaceRecognizer.Update(Emgu.CV.IInputArray,Emgu.CV.IInputArray)">
            <summary>
            Updates a FaceRecognizer with given data and associated labels.
            </summary>
            <param name="images">The training images, that means the faces you want to learn. The data has to be given as a VectorOfMat.</param>
            <param name="labels">The labels corresponding to the images</param>
        </member>
        <member name="M:Emgu.CV.Face.LBPHFaceRecognizer.Update``2(Emgu.CV.Image{``0,``1}[],System.Int32[])">
            <summary>
            Update the face recognizer with the specific images and labels
            </summary>
            <param name="images">The images used for updating the face recognizer</param>
            <param name="labels">The labels of the images</param>
        </member>
        <member name="T:Emgu.CV.Text.MCvERStat">
            <summary>
            The ERStat structure represents a class-specific Extremal Region (ER).
            An ER is a 4-connected set of pixels with all its grey-level values smaller than the values in its outer boundary. 
            A class-specific ER is selected (using a classifier) from all the ER’s in the component tree of the image.
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Pixel">
            <summary>
            Seed point
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Level">
            <summary>
            Threshold (max grey-level value)
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Area">
            <summary>
            Area
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Perimeter">
            <summary>
            Perimeter
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Euler">
            <summary>
            Euler number
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Rect">
            <summary>
            Bounding box
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.RawMoments0">
            <summary>
            Order 1 raw moments to derive the centroid
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.RawMoments1">
            <summary>
            Order 1 raw moments to derive the centroid
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.CentralMoments0">
            <summary>
            Order 2 central moments to construct the covariance matrix
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.CentralMoments1">
            <summary>
            Order 2 central moments to construct the covariance matrix
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.CentralMoments2">
            <summary>
            Order 2 central moments to construct the covariance matrix
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Crossings">
            <summary>
            Pointer to horizontal crossings
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.MedCrossings">
            <summary>
            Median of the crossings at three different height levels
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.HoleAreaRatio">
            <summary>
            Hole area ratio
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.ConvexHullRatio">
            <summary>
            Convex hull ratio
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.NumInflexionPoints">
            <summary>
            Number of inflexion points
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.Pixels">
            <summary>
            Pointer to pixels
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.probability">
            <summary>
            Probability that the ER belongs to the class we are looking for
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.ParentPtr">
            <summary>
            Pointer to the parent ERStat
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.ChildPtr">
            <summary>
            Pointer to the child ERStat
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.NextPtr">
            <summary>
            Pointer to the next ERStat
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.PrevPtr">
            <summary>
            Pointer to the previous ERStat
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.LocalMaxima">
            <summary>
            If or not the regions is a local maxima of the probability
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.MaxProbabilityAncestor">
            <summary>
            Pointer to the ERStat that is the max probability ancestor
            </summary>
        </member>
        <member name="F:Emgu.CV.Text.MCvERStat.MinProbabilityAncestor">
            <summary>
            Pointer to the ERStat that is the min probability ancestor
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.MCvERStat.GetCenter(System.Int32)">
            <summary>
            Get the center of the region
            </summary>
            <param name="imageWidth">The source image width</param>
            <returns>The center of the region</returns>
        </member>
        <member name="T:Emgu.CV.Text.ERFilter">
            <summary>
            Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.ERFilter.DisposeObject">
            <summary>
            Release all the unmanaged memory associate with this ERFilter
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.ERFilter.Run(Emgu.CV.IInputArray,Emgu.CV.Text.VectorOfERStat)">
            <summary>
            Takes image on input and returns the selected regions in a vector of ERStat only distinctive ERs which correspond to characters are selected by a sequential classifier
            </summary>
            <param name="image">Sinle channel image CV_8UC1</param>
            <param name="regions">Output for the 1st stage and Input/Output for the 2nd. The selected Extremal Regions are stored here.</param>
        </member>
        <member name="M:Emgu.CV.Text.ERFilter.ERGrouping(Emgu.CV.IInputArray,Emgu.CV.IInputArrayOfArrays,Emgu.CV.Text.VectorOfERStat[],Emgu.CV.Text.ERFilter.GroupingMethod,System.String,System.Single)">
            <summary>
            Find groups of Extremal Regions that are organized as text blocks.
            </summary>
            <param name="channels">Array of single channel images from which the regions were extracted</param>
            <param name="erstats">Vector of ER’s retrieved from the ERFilter algorithm from each channel</param>
            <param name="groupingTrainedFileName">The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml)</param>
            <param name="minProbability">The minimum probability for accepting a group.</param>
            <returns>The output of the algorithm that indicates the text regions</returns>
        </member>
        <member name="T:Emgu.CV.Text.ERFilterNM1">
            <summary>
            Extremal Region Filter for the 1st stage classifier of N&amp;M algorithm
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.ERFilterNM1.#ctor(System.String,System.Int32,System.Single,System.Single,System.Single,System.Boolean,System.Single)">
            <summary>
            Create an Extremal Region Filter for the 1st stage classifier of N&amp;M algorithm
            </summary>
            <param name="classifierFileName">The file name of the classifier</param>
            <param name="thresholdDelta">Threshold step in subsequent thresholds when extracting the component tree.</param>
            <param name="minArea">The minimum area (% of image size) allowed for retreived ER’s.</param>
            <param name="maxArea">The maximum area (% of image size) allowed for retreived ER’s.</param>
            <param name="minProbability">The minimum probability P(er|character) allowed for retreived ER’s.</param>
            <param name="nonMaxSuppression">Whenever non-maximum suppression is done over the branch probabilities.</param>
            <param name="minProbabilityDiff">The minimum probability difference between local maxima and local minima ERs.</param>
        </member>
        <member name="T:Emgu.CV.Text.ERFilterNM2">
            <summary>
            Extremal Region Filter for the 2nd stage classifier of N&amp;M algorithm
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.ERFilterNM2.#ctor(System.String,System.Single)">
            <summary>
            Create an Extremal Region Filter for the 2nd stage classifier of N&amp;M algorithm
            </summary>
            <param name="classifierFileName">The file name of the classifier</param>
            <param name="minProbability">The minimum probability P(er|character) allowed for retreived ER’s.</param>
        </member>
        <member name="T:Emgu.CV.Text.VectorOfERStat">
            <summary>
            Wrapped class of the C++ standard vector of ERStat.
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.#ctor(System.Runtime.Serialization.SerializationInfo,System.Runtime.Serialization.StreamingContext)">
            <summary>
            Constructor used to deserialize runtime serialized object
            </summary>
            <param name="info">The serialization info</param>
            <param name="context">The streaming context</param>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.GetObjectData(System.Runtime.Serialization.SerializationInfo,System.Runtime.Serialization.StreamingContext)">
            <summary>
            A function used for runtime serialization of the object
            </summary>
            <param name="info">Serialization info</param>
            <param name="context">Streaming context</param>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.#ctor">
            <summary>
            Create an empty standard vector of ERStat
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.#ctor(System.Int32)">
            <summary>
            Create an standard vector of ERStat of the specific size
            </summary>
            <param name="size">The size of the vector</param>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.#ctor(Emgu.CV.Text.MCvERStat[])">
            <summary>
            Create an standard vector of ERStat with the initial values
            </summary>
            <param name="values">The initial values</param>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.Push(Emgu.CV.Text.MCvERStat[])">
            <summary>
            Push an array of value into the standard vector
            </summary>
            <param name="value">The value to be pushed to the vector</param>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.ToArray">
            <summary>
            Convert the standard vector to an array of ERStat
            </summary>
            <returns>An array of ERStat</returns>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.Clear">
            <summary>
            Clear the vector
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.DisposeObject">
            <summary>
            Release the standard vector
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.GetInputArray">
            <summary>
            Get the pointer to cv::_InputArray
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.GetOutputArray">
            <summary>
            Get the pointer to cv::_OutputArray
            </summary>
        </member>
        <member name="M:Emgu.CV.Text.VectorOfERStat.GetInputOutputArray">
            <summary>
            Get the pointer to cv::_InputOutputArray
            </summary>
        </member>
        <member name="P:Emgu.CV.Text.VectorOfERStat.Size">
            <summary>
            Get the size of the vector
            </summary>
        </member>
        <member name="P:Emgu.CV.Text.VectorOfERStat.StartAddress">
            <summary>
            The pointer to the first element on the vector. In case of an empty vector, IntPtr.Zero will be returned.
            </summary>
        </member>
        <member name="P:Emgu.CV.Text.VectorOfERStat.Item(System.Int32)">
            <summary>
            Get the item in the specific index
            </summary>
            <param name="index">The index</param>
            <returns>The item in the specific index</returns>
        </member>
        <member name="T:Emgu.CV.BgSegm.BackgroundSubtractorGMG">
            <summary>
            Background Subtractor module based on the algorithm given in:
            Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg, 
            “Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation”, 
            American Control Conference, Montreal, June 2012.
            </summary>
        </member>
        <member name="M:Emgu.CV.BgSegm.BackgroundSubtractorGMG.#ctor(System.Int32,System.Double)">
            <summary>
            Create a background subtractor module based on GMG
            </summary>
            <param name="initializationFrames">Number of frames used to initialize the background models.</param>
            <param name="decisionThreshold">Threshold value, above which it is marked foreground, else background.</param>
        </member>
        <member name="M:Emgu.CV.BgSegm.BackgroundSubtractorGMG.DisposeObject">
            <summary>
            Release all the unmanaged memory associated with this background model.
            </summary>
        </member>
        <member name="T:Emgu.CV.BgSegm.BackgroundSubtractorMOG">
            <summary>
            Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
            The class implements the following algorithm:
            "An improved adaptive background mixture model for real-time tracking with shadow detection"
            P. KadewTraKuPong and R. Bowden,
            Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
            http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
            </summary>
        </member>
        <member name="M:Emgu.CV.BgSegm.BackgroundSubtractorMOG.#ctor(System.Int32,System.Int32,System.Double,System.Double)">
            <summary>
            Create an "Improved adaptive Gaussian mixture model for background subtraction".
            </summary>
            <param name="history">The length of the history.</param>
            <param name="nMixtures">The maximum number of gaussian mixtures.</param>
            <param name="backgroundRatio">Background ratio</param>
            <param name="noiseSigma">Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.</param>
        </member>
        <member name="M:Emgu.CV.BgSegm.BackgroundSubtractorMOG.DisposeObject">
            <summary>
            Release all the unmanaged memory associated with this background model.
            </summary>
        </member>
    </members>
</doc>
