EyesWeb 5.0.3 Gesture Processing User Manual

EyesWeb XMI 5.0.3.0 – Expressive Gesture processingLibrary

July 30, 2009

Part I

Description

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The EyesWeb Expressive Gesture Processing Library includes a collection of softwaremodules and patches (interconnections of modules) contained ....

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Part II

Reference

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Chapter 1

Gesture Processing Catalog

1.1 Blocks

1.1.1 Add point

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class name Add pointcatalog name Gesture Processingcatalog id EywGPclass id AddPointauthors Gualtiero Volpe

This block appends the input point to the input trajectory.

Inputs

Input trajectoryid InputTrajectorytype Kernel, Generic datatypetype id kernel, generic datatype

requiredrequired for initializationrequired for execution

read only/read write read writereferred as inplace Output trajectoryreferred as inherited *no*

The trajectory to which the input point has to be added.

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Input pointid InputPointtype Kernel, Generic datatypetype id kernel, generic datatype


read only/read write read onlyreferred as inplace *no*referred as inherited *no*

The point to be added to the input trajectory.

Outputs

Output trajectoryid OutputTrajectorytype Kernel, Generic datatypetype id kernel, generic datatypeinplace id Input trajectoryinherited id *no*

The output trajectory including the added point.

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1.1.2 Blob ellipse

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class name Blob ellipsecatalog name Gesture Processingcatalog id EywGPclass id BlobEllipseauthors Gualtiero Volpe

This block generates an elliptical approximation of a blob. From such ellipse it is possibleto obtain an estimate of parameters such as blob orientation and contraction/expansion.

Inputs

Input imageid InputImagetype Base, Blob 2Dtype id base, blob2d



The input image containing the blob to be approximated by an ellipse. It must be ablack & white image.

Outputs

Output ellipseid OutputEllipsetype Base, Ellipse 2D doubletype id base, ellipse 2d doubleinplace id *no*inherited id *no*

The ellipse approximating the input block.

Output ellipseid OutputCentertype Base, Graphic Point 2D doubletype id base, graphic point 2d doubleinplace id *no*inherited id *no*

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The ellipse approximating the input block.

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1.1.3 Contraction Index

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class name Contraction Indexcatalog name Gesture Processingcatalog id EywGPclass id ContractionIndexauthors Gualtiero Volpe

This block computes the Contraction Index (CI) of a blob.

Inputs

Input blobid InputBlobtype Base, Blob 2Dtype id base, blob2d



The input blob whose Contraction Index has to be computed.

Outputs

Contraction Indexid OutputIndextype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

The computed Contraction Index.

Parameters

Contraction Indexid ComputeBlobCItype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Contraction Index

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MinHeightRectid ComputeMinHeightRectRatiotype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

MinWidthRectid ComputeMinWidthRectRatiotype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Compute Eccentricityid ComputeEllipseEccentricitytype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Resetid Resettype Kernel, Trigger datatype (Kernel Catalog).type id kernel, trigger

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1.1.4 ConvexHull

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class name ConvexHullcatalog name Gesture Processingcatalog id EywGPclass id convexhullcomputation

Computes the convex hull of the input contour.

Inputs

Input contourid ConvexHullInContourtype Gesture Processing, Integer 2D Graphic Contourtype id EywGP, IntGraphicContour



A contour. This block computes the convex hull of the input contour

Outputs

The convex hullid ConvexHullOutConvexHulltype Base, Graphic Polygon 2D inttype id base, graphic polygon 2d intinplace id *no*inherited id *no*

This is the convex hull computed by the block

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1.1.5 Empty trajectory

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class name Empty trajectorycatalog name Gesture Processingcatalog id EywGPclass id EmptyTrajectoryGeneratorauthors Gualtiero Volpe

This block generates an empty trajectory.

Outputs

Output trajectoryid OutputTrajectorytype Gesture Processing, Integer 2D Geometric Trajectorytype id EywGP, IntGeometricTrajectory2Dinplace id *no*inherited id *no*

The generated trajectory. It will be an empty trajectory.

Parameters

Trajectory typeid TrajectoryTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:Geometric Integer 2DGeometric Real 2DGraphic Integer 2DGraphic Real 2DGeometric Integer 3DGeometric Real 3D

domain [ 0, 6 )The type of trajectory to be generated. It may be 2D, 3D or nD, geometric or graphical,

interger or real.

Trajectory labelid Labeltype Kernel, String datatype (Kernel Catalog).type id kernel, string

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The label of the generated 2D trajectory.

Sampling rateid SamplingRatetype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The sampling rate in Hz at which the trajectory is generated and its points are sampled.

Is maximum of points fixed?id IsSizeFixedtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Initialization matrix parameter is added to the block and displayed.The potential function will be initialized according to the values contained in the matrixprovided for the Initialization matrix parameter. Otherwise the potential function is ini-tialized with zeros. The default value is false.

Maximum number of pointsid MaxSizetype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 2, +infinity )

The maximum number of points in the generated trajectory. This parameter is availableonly if the maximum number of points is fixed.


Reset the generated trajectory by removing all its points.

Colorid Colortype Kernel, RGBColor datatype (Kernel Catalog).type id kernel, rgbcolor

The color of the generated trajectory. The value of this parameter is taken into accountonly if the generated trajectory is a graphic trajectory.

Thicknessid Thicknesstype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The thickness of the generated trajectory. The value of this parameter is taken intoaccount only if the generated trajectory is a graphic trajectory.

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1.1.6 Extract contours

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class name Extract contourscatalog name Gesture Processingcatalog id EywGPclass id ExtractContoursauthors Gualtiero Volpe

This block extracts the contours of the blobs detected in the input image. It returnsthe (x,y) coordinates of all the points in the extracted contours.

Inputs

Input imageid InputBlobtype Base, Blob 2Dtype id base, blob2d



The input image containing the blobs whose contours have to be extracted.

Outputs

Extracted contoursid ExtractedContourstype Kernel, Labelled settype id kernel, labeled setinplace id *no*inherited id *no*

The extracted contours of the blobs detected in the input image.

Parameters

Extract contoursid ExtractContourstype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Extract contours

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Extract outer contourid ExtractOuterContourstype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Extract outer contour

Contour typeid ContourTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:Geometric Integer 2DGeometric Real 2DGraphic Integer 2DGraphic Real 2D

domain [ 0, 4 )Specifies the type of data used for the output contour

Minimum number of pointsid MinNumOfPointstype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The minimum number of points for a contour in order to be considered.

Approximation methodid ApproxMethodtype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:NoneSimpleTC89 L1TC89 KCOS

domain [ 0, 4 )The approximation method to be used for extracting contours.



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IsFilledid IsFilledtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Specifies whether the contour has to be filled

FillColorid FillColortype Kernel, RGBColor datatype (Kernel Catalog).type id kernel, rgbcolor

Specifies the color to be used to fill the contour

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1.1.7 GeneralisedAutoCorrelation

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class name GeneralisedAutoCorrelationcatalog name Gesture Processingcatalog id EywGPclass id generalised auto correlationauthors Paolo Coletta

Computes the Generalised Auto-Correlation function from a static time-series. For de-tails about the Generalised Auto-Correlation function see ”Recurrence plots for the analysisof complex systems” by Norbert Marwan, M. Carmen Romano, Marco Thiel, and JurgenKurths; in Physics Reports 438 (2007), pages 237-329. This block implements equation(50) pag. 265 or equivalently (111) pag. 289. The value of epsilon is user-specified, N isthe buffer-size of the input time-series. The user can specify both a specific value for tauor a range. In the first case the output is a scalar number which gives the value of theauto-correlation for the given tau and epsilon; in the second case the output is the functionRR(tau).

Inputs

Input signalid inputtype Kernel, Generic datatypetype id kernel, generic datatype



Input signal on which to compute the generalised auto-correlation; the input signalshould implement the IStaticTimeSeries interface.

Required interfacesKernel, StaticTimeSeries

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Outputs

RR function of Tauid output function of tautype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

This output represents the value of the function RR for each possible Tau in the user-specified domain

Parameters

Epsilonid epsilontype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Specifies the upper bound of the distance of two near points. I.e., two point are consid-ered to be one near the other one if their distance is belowe this threashold. The value ofR in the sum is 1 for near points.

Output typeid parameter output typetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:ScalarFunction Of Tau

domain [ 0, 2 )Specifies whether the output is a Scalar value, which represents the value of RR(epsilon,tau)

for for given values of epsilon and tau, or a Function, which represent the value of RR(epsilon,*)for a given value of epsilon and all possible values of tau

Tau Minid parameter tau mintype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

This parameter represents the minimum value of tau for which the output functionRR(tau) is computed

Tau Spreadid parameter tau spreadtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

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This parameter represents the spread of tau, i.e., tau is asusmed to be in the range [TauMin, Tau Min + Tau Spread]. This determines the size of the output function of tau.

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1.1.8 Geometric Trajectory from points

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class name Geometric Trajectory from pointscatalog name Gesture Processingcatalog id EywGPclass id GeometricTrajectoryFromPointsauthors Gualtiero Volpe

This block generates a trajectory conisting of the temporal sequence of the points pro-vided as input.

Inputs

Input 2D point.id InputPointtype Base, Graphic Labelled Set 2D inttype id base, graphic labeled set 2d int



The input 2D point to be added to the generated trajectory.

Outputs

Output 2D trajectoryid OutputTrajectorytype Base, Graphic Labelled Set 2D inttype id base, graphic labeled set 2d intinplace id *no*inherited id *no*

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Parameters


layout



interger or real.




The sampling rate in Hz at which the input points are sampled.


Set this parameter to true if the maximum number of points belonging to the trajectoryis fixed. When such number is reached the oldest points are removed from the trajectory.



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1.1.9 Get kinematical features

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class name Get kinematical featurescatalog name Gesture Processingcatalog id EywGPclass id GetKinematicalFeaturesauthors Gualtiero Volpe

This block computes kinematical features (e.g., velocity, acceleration) on the input tra-jectory.

Inputs




The trajectory to which the input point has to be added.

Outputs

Output trajectoryid OutputFeaturetype Kernel, Generic datatypetype id kernel, generic datatypeinplace id *no*inherited id *no*

The output trajectory including the added point.

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Parameters

Featureid FeatureToExtracttype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:VelocityAccelerationDirection

domain [ 0, 3 )The kinematical feature to be extracted.

Numeric derivativeid NumericDerivativetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:Backward differenceCentral difference

domain [ 0, 2 )The numeric derivative algorithm to be applied.

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1.1.10 Get point

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class name Get pointcatalog name Gesture Processingcatalog id EywGPclass id GetPointauthors Gualtiero Volpe

This block retrieves the required point from the input trajectory. Note that the pointis not removed from the trajectory.

Inputs




The input trajectory from where the indicated point will be retrieved.

Outputs

Output pointid OutputPointtype Kernel, Generic datatypetype id kernel, generic datatypeinplace id *no*inherited id *no*

The retrieved output point.

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Parameters

Point to extractid GetPointModetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:Get latest pointGet oldest pointGet point with index

domain [ 0, 3 )Indicate here which point you want to extract: the latest, the oldest, or a point to be

identified by its numerical index.

Indexid Indextype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The index of the point to be retrieved. It ranges from 0 to the current size of thetrajectory - 1. This parameter is enabled only if the ”Point to extract” parameter is set to”Get point with index”.

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1.1.11 Get properties

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class name Get propertiescatalog name Gesture Processingcatalog id EywGPclass id GetTrajectoryPropertiesauthors Gualtiero Volpe

This block retrives and returns the properties (e.g., label, sampling rate, number ofpoints) of the input trajectory.

Inputs




The input trajectory whose propeties have to be retrieved and returned.

Outputs

Number of pointsid Sizetype Kernel, Int datatype (Kernel Catalog).type id kernel, intinplace id *no*inherited id *no*

The current number of points in the input trajectory.

Parameters

Return potential matrixid OutputTypetype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

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If set to true the Potential function output becomes available and the whole potentialmatrix is returned by the block. The default value is true..

Return hit regionsid OutputLabeltype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Hit regions output becomes available and a vector is returned, whereeach item is set to 0 if the corresponding region is missed or to 1 if the corresponding regionis hit. The default value is false.

Return indexes of hit regionsid OutputSamplingRatetype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true the Hit regions indexes output becomes available and a list of indexes ofhit regions is returned by the block. The default value is false.

Return region valuesid OutputIsSizeFixedtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Region values output becomes available and a vector is returned,where each item is set to the current potential value for the corresponding region. Thedefault value is false.

Return region valuesid OutputSizetype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool


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1.1.12 Get trajectory features

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class name Get trajectory featurescatalog name Gesture Processingcatalog id EywGPclass id GetTrajectoryFeaturesauthors Gualtiero Volpe

This block retrives and returns features (e.g., lenght, directness index) of the inputtrajectory.

Inputs




The input trajectory whose features have to be computed.

Outputs

Output featureid OutputFeaturetype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

The computed output feature.

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Parameters

Feature to extractid FeatureToExtracttype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:LengthDirectness index

domain [ 0, 2 )The feature to be extracted.

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1.1.13 Hit Cell Detector

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class name Hit Cell Detectorcatalog name Gesture Processingcatalog id EywGPclass id HitCellDetectorauthors Gualtiero Volpe

Given a model in which a 2D space is divided in cells, this block computes the cellsoccupied (hit) by the current input points.

Inputs

Input positionsid InputPositionstype Kernel, Listtype id kernel, list



The (x, y) coordinates of the input positions.

Outputs

Hit cellsid HitCellstype Kernel, Listtype id kernel, listinplace id *no*inherited id *no*

The (i, j) indexes of the hit cells corresponding to the current input positions.

Parameters

Cells along xid CellsXtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has to be divided along the x dimension.

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Cells along yid CellsYtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has to be divided along the y dimension.

Minimum along xid XMintype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The minimum input value along the x dimension.

Maximum along xid XMaxtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The maximum input value along the x dimension.

Minimum along yid YMintype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The minimum input value along the y dimension.

Maximum along yid YMaxtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The maximum input value along the y dimension.

Clip valuesid ClipValuestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set, out of range values are clipped to the provided maximum and minimum values.Otherwise a warning message is displayed and the hit cell is not computed.

Clear input listid ClearListtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set, the input list is cleared after the computation of the hit cells.

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Quantization algorithmid QuantAlgtype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:Linear uniform

domain [ 0, 1 )The quantization algorithm to be employed for computing the hit cell.

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1.1.14 Hit Region Detector

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class name Hit Region Detectorcatalog name Gesture Processingcatalog id EywGPclass id HitRegionDetectorauthors Gualtiero Volpe

Given a model in which a 2D space is divided in cells, this block computes which regions(groups of cells) are occupied (hit) by the silhouette of a blob provided as input. Regionsare defined through a mask provided as parameter. Cells correspond to pixels in the imagefrom which the blob silhouette is extracted.

Inputs

Input imageid InputImagetype Base, Imagetype id base, image



An image containing the silhouette of a blob. The block will compute which regions inthe 2D space the input blob occupies (hits). The input image must be black and white,single channel, and have 8-bit depth.

Outputs

Hit regionsid OutputListtype Kernel, Listtype id kernel, listinplace id *no*inherited id *no*

A list of integer numbers. Such numbers are the indexes of regions the input bloboccupies (hits). The indexes are defined in the mask provided as parameter.

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Parameters

Maskid Masktype Kernel, Integer matrix.type id kernel, int-matrix

An integer matrix containing the indexes (identifiers) of the regions defined in the space.Items labeled with the same index (integer value) belong to the same region. The rangeof such indexes is [0, MaxNumOfRegions -1] where MaxNumOfRegions is the maximumnumber of regions that can be defined. The block considers as maximum number of regionsthe size of the Thresholds vector (see below). The mask matrix should have the samedimensions of the image from which the silhouette of the input blob has been extracted.The default dimensions are 288 rows and 352 columns; the default value is a matrix ofzeros.

Thresholdsid Thresholdstype Kernel, Integer matrix.type id kernel, int-matrix

An integer vector containing the thresholds to be applied to the occupation values ofeach region. Each item is the threshold for the corresponding region. A region is consideredto be hit if the number of its cells the input blob occupies exceeds this threshold. The blockconsiders the size of this vector as the maximum number of regions that can be defined.The default dimensions are 1 row and 256 columns (corresponding to the definition of 256cells). The default value is a vector of zeros.

Return occupation valuesid OutOccValuestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true the block returns both the list of the indexes (identifiers) of the hit regionsand, separately, the list of the indexes and of the occupation values of the hit regions.Otherwise the list of the indexes of the hit regions only is returned. The default value isfalse.

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1.1.15 InternalQuantityOfMotion

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class name InternalQuantityOfMotioncatalog name Gesture Processingcatalog id EywGPclass id quantity of motion

Calculate internal quantity of motion. The measuring reguards internal modifications ofthe silhouette. The algortihm needs both image and blob input.ERROR AND WARNINGMESSAGE. - During initialization phase: - Matrix and image must be the same dimension:if the dimension of the matrix of weight and the dimension of the image are different. -Block Internal QOM : The mask or the anchor size are invalid: if the dimensions of medianfilter are not odd. - During execution phase: - Ipp library info: ippiv81.lib 5.2 5.2.108.410-Ipp Error description : Invalid mask size.

Inputs

input blobid input blobtype Base, Blob 2Dtype id base, blob2d


read only/read write read writereferred as inplace *no*referred as inherited *no*

This is the blob extracted from the input image. Actually the block works only with asingle blob.

input imageid input imagetype Base, Imagetype id base, image



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Input image.

Outputs

output qomid output qomtype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

Quantity of internal motion. The output represents the number of nonzero pixels ofQOM image. If the parameter ’type’ is set to int, then the output is the number of pixels.If it is set to double the output is normalized according to ’Normalization Type’ parameter

output imageid output imagetype Base, Imagetype id base, imageinplace id *no*inherited id *no*

The image represents differents beetween the first and the last image of a dynamicqueue. The lenght of the queue is expressed by the parameter ’Window lenght’

Parameters

Weightid output qomtype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:EuclideaEuclidea InversaFrom MatrixFrom Matrix InverseNone

domain [ 0, 5 )The parameter represent the type of weight. Euclidean: the weight of pixels is the

distance from the baricenter. Inverse Euclidean: the weight of pixels is the inverse of thedistance from the baricenter. From Matrix: weights of pixels are stored in the matrix .Inverse From Matrix: weight of pixels are the inverse of the matix. None: There is noweight for each pixel.

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WindowLenghtid window lenghttype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The size of a dynamic queue. The queue stores images and the algorithm process thefirst and the last image in the buffer.

Thresholdid thresholdtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The threshold used in the binarization of image.

Resetid resettype Kernel, Trigger datatype (Kernel Catalog).type id kernel, trigger

Reset the method and clear the queue of images. When reset button is pressed thealgorithmneeds to read ’Window lenght’ images.

MedianOnOffid mediantype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Active median filter.

MedianXid median xtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The width of median filter mask used in the method

MedianYid median ytype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The height of median filter mask used in the method

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Typeid output qom typetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:IntDouble

domain [ 0, 2 )Type of QOM. If it’s set to int value then the output is the number of non zero pixels in

the output image. If it’s set to double value the output is the normalization of the numberof non zero pixels according to the parameter Normalization Type.

Normalization Typeid norm qomtype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:AreaofImageAreaofBoundingRectangleAreaofBlob

domain [ 0, 3 )It specifies if the numeric output is normalized by the area of entire image, by the area

of the bounding rectangle or by the area of the blob.

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1.1.16 Motion duration

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class name Motion durationcatalog name Gesture Processingcatalog id EywGPclass id MotionDurationauthors Gualtiero Volpe

This block computes the time duration of pause and motion phases starting from asegmentation signal provided as input.

Inputs

Input segmentation signalid SegmentationSignaltype Kernel, Double datatype (Kernel Catalog).type id kernel, double



The input segmentation signal. It should be 0 during pause phases and different from0 during motion phases.

Outputs

Motion durationid MotionDurationtype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

The duration of the last motion phase.

Pause durationid PauseDurationtype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

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The duration of the last pause phase.

Parameters

Measure unitid MeasureUnittype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:FramesMilliseconds

domain [ 0, 2 )The measure unit for the output durations. It can be frames or milliseconds.


The sampling rate of the input segmentation signal. This parameter is enabled only ifthe output measure unit is milliseconds.

Continuous outputid ContinuousOutputtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If this parameter is set to false the output is returned only at the end of a motion orpause phase. Otherwise, the last value is continuously returned.

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1.1.17 PixelWeightQuantityOfMotion

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class name PixelWeightQuantityOfMotioncatalog name Gesture Processingcatalog id EywGPclass id pixel weight quantity of motion

Calculate quantity of motion. The block can weight pixels according to parameterweight. — ERROR AND WARNING — - During Initilalization phase. - Matrix and imagemust be the same dimension: if the dimension of the matrix of weight jand the dimensionof the image are different. - Block Internal QOM : The mask or the anchor size are invalid:if the dimensions of median filter are not odd. - During execution phase: - Ipp library info:ippiv81.lib 5.2 5.2.108.410- Ipp Error description : Invalid mask size.

Inputs

input blobid input blobtype Base, Blob 2Dtype id base, blob2d



This is the blob extracted from the input image. Actually the block works only with asingle blob.

Outputs

output qomid output qomtype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

Value of qom

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output imageid output imagetype Base, Imagetype id base, imageinplace id *no*inherited id *no*

The image represents differents beetween the first and the last image of a dynamicqueue. The lenght of the queue is expressed by the parameter ’Window lenght’

Parameters

Weightid output qom typetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:EuclideanInverse EuclideanFrom MatrixInverse From MatrixNone

domain [ 0, 5 )The parameter represent the type of weight. Euclidean: the weight of pixels is the

distance from the baricenter. Inverse Euclidean: the weight of pixels is the inverse of thedistance from the baricenter. From Matrix: weights of pixels are stored in the matrix .Inverse From Matrix: weight of pixels are the inverse of the matix. None: There is noweight for each pixel.

WindowLenghtid window lenghttype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The size of a dynamic queue. The queue stores images and the algorithm process thefirst and the last image in the buffer.

Thresholdid thresholdtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The threshold used in the binarization of image.

Resetid resettype Kernel, Trigger datatype (Kernel Catalog).type id kernel, trigger

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Reset the method and clear the queue of images. When reset button is pressed thealgorithmneeds to read ’Window lenght’ images.

Normalization Typeid normalization;type Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:AreaofImageAreaofBoundingRectangleAreaofBlob

domain [ 0, 3 )It specifies if the numeric output is normalized by the area of entire image, by the area

of the bounding rectangle or by the area of the blob.

MedianOnOffid mediantype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Active median filter.

MedianXid median xtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The width of median filter mask used in the method.

MedianYid median ytype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 0, +infinity )

The height of median filter mask used in the method.

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1.1.18 Position Depending Potentials

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class name Position Depending Potentialscatalog name Gesture Processingcatalog id EywGPclass id PositionDependingPotentialsauthors Gualtiero Volpe

Given a model in which a 2D space is divided in cells, this block computes a 2D potentialfunction depending on the indexes (h, k) of the cells received as input. The kind of potentialfunction can be provided as parameter.

Inputs

Input cellsid InputPositionstype Kernel, Listtype id kernel, list



A list of input cells used for computing the potential function. This list contains acollection of 1x2 or 2x1 vectors whose items are the (h, k) indexes of a cell in the space.Usually, this input comes from an Hit Cell Detector and these are the cells occupied by acollection of input points.

Outputs

id OutputPotentialtype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

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Parameters

Cells along Xid CellsAlongXtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has been divided along the x dimension. Itcorresponds to the number of columns in the output potential matrix. If the block receivesits input from a Hit Cell Detector, this parameter should have the same value than thecorresponding parameter in the Hit Cell Detector. The default value is 10.

Cells along Yid CellsAlongYtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has been divided along the y dimension.It corresponds to the number of rows in the output potential matrix. If the block receivesits input from a Hit Cell Detector, this parameter should have the same value than thecorresponding parameter in the Hit Cell Detector. The default value is 10.

Potential typeid PotentialTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:Linear increment/decrementPolynomialOccupation ratesGaussianLogarithmic

The kind of potential to be computed. The following options are available at the mo-ment: linear increment /decrement, polynomial, occupation rates, Gaussian, logarithmic.

Use initialization matrixid UseInitMatrixtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Initialization matrix parameter is added as parameter to the blockand displayed. The potential function will be initialized according to the values containedin the matrix provided for the Initialization matrix parameter. Otherwise the potentialfunction is initialized with zeros. The default value is false.

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Each time a reset command is triggered, the potential function is reinitialized: the newinitial values are those contained in the Initialization matrix or zeros, depending on the Useinitialization matrix parameter.

Increment factorid IncrementFactortype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The increment factor to be applied to the hit cell. For each cell in the input list thisincrement factor is applied. The default value is 1.

Decrement factorid DecrementFactortype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The decrement factor to be applied to missed cells. For each cell which is not in theinput list this decrement factor is applied. The default value is 1.

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1.1.19 Region Depending Potentials

bitmap

class name Region Depending Potentialscatalog name Gesture Processingcatalog id EywGPclass id RegionDependingPotentialsauthors Gualtiero Volpe

Given a model in which a 2D space is divided in cells, this block computes a 2D potentialfunction depending on regions defined in such a space and, in particular, on the regionsthat are occupied by points or to which the cells received as input belong. The kind ofpotential function can be provided as parameter.

Inputs

Input listid InputPositionstype Kernel, Listtype id kernel, list



A list of either input cells or indexes of hit (occupied) regions. In the former case, thislist contains a collection of 1x2 or 2x1 vectors whose items are the (h, k) indexes of a cellin the space. In the latter, this is a list of integer numbers (region indexes). This inputusually comes from a Hit Cell Detector (and in such a case it is a list of input cells) or froma Hit Region Detector (and in such a case it is list of region indexes). If the input is a listof cells, hit regions indexes are automatically computed.

Outputs

Potential functionid Potentialtype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

A matrix representing the computed potential function. The number of items in thismatrix is equal to the number of cells the space is divided in. Therefore, the number of rows

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is equal to the number of cells along y and the number of columns is equal to the numberof cells along x. The output is available only if the Return potential matrix parameter isset to true.

Parameters

Cells along Xid CellsAlongXtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has been divided along the x dimension. Itcorresponds to the number of columns in the output potential matrix. If the block receivesits input from a Hit Cell Detector, this parameter should have the same value than thecorresponding parameter in the Hit Cell Detector. The default value is 10.

Cells along Yid CellsAlongYtype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The number of cells in which the input space has been divided along the y dimension.It corresponds to the number of rows in the output potential matrix. If the block receivesits input from a Hit Cell Detector, this parameter should have the same value than thecorresponding parameter in the Hit Cell Detector. The default value is 10.

Potential typeid PotentialTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:Linear increment/decrementLinear occupation ratesExponential occupation rates

The kind of potential to be computed. The following options are available at the mo-ment: linear increment /decrement, polynomial, occupation rates, Gaussian, logarithmic.

Use initialization matrixid UseInitMatrixtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Initialization matrix parameter is added as parameter to the blockand displayed. The potential function will be initialized according to the values containedin the matrix provided for the Initialization matrix parameter. Otherwise the potentialfunction is initialized with zeros. The default value is false.

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Each time a reset command is triggered, the potential function is reinitialized: the newinitial values are those contained in the Initialization matrix or zeros, depending on the Useinitialization matrix parameter.

Regions maskid Masktype Kernel, Integer matrix.type id kernel, int-matrix

An integer matrix containing the indexes (identifiers) of the regions defined in the space.Items labeled with the same index (integer value) belong to the same region. The rangeis [0, MaxNumOfRegions -1] where MaxNumOfRegions is the maximum number of regionsthat can be defined. Such value is provided through the (Maximum) number of regionsparameter. The size of the mask matrix is automatically computed and it is the samesize of the matrix of the potential function, i.e., the number of rows is the number of cellsalong y and the number of columns is the number of cells along y. In case the input comesfrom a Hit Region Detector, this mask should be the same mask provided to the Hit regiondetector and the number of cells and regions to be set accordingly. The default value is amatrix of zeros.

(Maximum) Number of regionsid NumOfRegionstype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

The maximum number of regions that can be defined. Regions will be labeled withprogressive integer numbers from 0 up to this value minus one. In case the input of theblock comes from a Hit Region Detector, this parameter should be set equal to the size ofThresholds vector in the Hit Region Detector. The default value is 10.

Use initialization values for regionsid UseInitValForRegtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Initialization values for regions parameter is added to the block anddisplayed. The potential function will be initialized according to the values contained in thevector provided for the Initialization values for regions parameter. Otherwise the potentialfunction is initialized with zeros. Since just one way of initializing the potential function isallowed, if the Use initialization matrix parameter is set to true, this one is automaticallyset to false. The default value is false.

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Return potential matrixid OutputPotentialMatrixtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true the Potential function output becomes available and the whole potentialmatrix is returned by the block. The default value is true..

Return hit regionsid OutputHitRegionstype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the Hit regions output becomes available and a vector is returned, whereeach item is set to 0 if the corresponding region is missed or to 1 if the corresponding regionis hit. The default value is false.

Return indexes of hit regionsid OutputHitRegionIndexestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true the Hit regions indexes output becomes available and a list of indexes ofhit regions is returned by the block. The default value is false.

Return region valuesid OutputRegionValuestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool


Increment factorid IncrementFactortype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The increment factor to be applied to hit regions. In case of list of cells as input, foreach cell in the input list this increment factor is applied to the region it belongs to. Thedefault value is 1.

Decrement factorid DecrementFactortype Kernel, Double datatype (Kernel Catalog).type id kernel, double

The decrement factor to be applied to missed regions. In case of list of cells as input,for each cell which is not in the input list this decrement factor is applied to the region itbelongs to. The default value is 1.

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1.1.20 Remove point

bitmap

class name Remove pointcatalog name Gesture Processingcatalog id EywGPclass id RemovePointauthors Gualtiero Volpe

This block retrieves the oldest point from the input trajectory and removes it.

Inputs



read only/read write read writereferred as inplace Output pointreferred as inherited *no*

The input trajectory from where the indicated point will be retrieved.

Outputs

Output pointid OutputTrajectorytype Kernel, Generic datatypetype id kernel, generic datatypeinplace id Input trajectoryinherited id *no*


Output pointid OutputPointtype Kernel, Generic datatypetype id kernel, generic datatypeinplace id *no*inherited id *no*


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1.1.21 Set properties

bitmap

class name Set propertiescatalog name Gesture Processingcatalog id EywGPclass id SetTrajectoryPropertiesauthors Gualtiero Volpe

This block modifies the properties (label, sampling rate, etc.) of the input trajectory.

Inputs



read only/read write read writereferred as inplace *no*referred as inherited Output trajectory

The input trajectory whose properties have to be changed.

Outputs

Output trajectoryid OutputTrajectory2type Kernel, Generic datatypetype id kernel, generic datatypeinplace id Input trajectoryinherited id *no*

The output trajectory with the modified properties.

Parameters

Modify labelid ModifyLabeltype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Set this parameter to true if you want to change the trajectory label.

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Modify sizeid ModifySizetype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Set this parameter to true if you want to change the trajectory sizel.


The label of the trajectory.





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1.1.22 Trajectory from points

bitmap

class name Trajectory from pointscatalog name Gesture Processingcatalog id EywGPclass id TrajectoryFromPointsauthors Gualtiero Volpe

This block generates a trajectory conisting of the temporal sequence of the points pro-vided as input.

Inputs

Input 2D point.id InputPointtype Base, Point 2D inttype id base, point 2d int



The input 2D point to be added to the generated trajectory.

Outputs

Output 2D trajectoryid OutputTrajectorytype Gesture Processing, Integer 2D Geometric Trajectorytype id EywGP, IntGeometricTrajectory2Dinplace id *no*inherited id *no*

The generated 2D trajectory

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Parameters


layout



interger or real.




The sampling rate in Hz at which the input points are sampled.





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1.2 Datatypes

1.2.1 Double 2D Geometric Contour

class name Double 2D Geometric Contourcatalog name Gesture Processingcatalog id EywGPclass id DoubleGeometricContour

This datatype stores 2D contours and provides information about them.

Supported interfaces

• kernel, trasformation 2d

• EywGP, ContourBase

• EywGP, Contour2DBase

• EywGP, GeometricContour2DBase

• EywGP, ContourAttributes

1.2.2 Double 2D Geometric Trajectory

class name Double 2D Geometric Trajectorycatalog name Gesture Processingcatalog id EywGPclass id DoubleGeometricTrajectory2D

This datatype stores 2D trajectories and provides information about them (e.g., kine-matical features, expressive features).


• Base, Transformations2D

• EywGP, TrajectoryBase

• EywGP, Trajectory2DBase

• EywGP, GeometricTrajectory2DBase

• EywGP, TrajectoryAttributes

• EywGP, TrajectoryFeatures

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1.2.3 Double 2D Graphic Contour

class name Double 2D Graphic Contourcatalog name Gesture Processingcatalog id EywGPclass id DoubleGraphicContour




• Base, Drawing



• EywGP, GraphicContour2DBase


1.2.4 Double 2D Graphic Trajectory

class name Double 2D Graphic Trajectorycatalog name Gesture Processingcatalog id EywGPclass id DoubleGraphicTrajectory2D




• Base, Drawing



• EywGP, GraphicTrajectory2DBase



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1.2.5 Double 3D Geometric Trajectory

class name Double 3D Geometric Trajectorycatalog name Gesture Processingcatalog id EywGPclass id DoubleGeometricTrajectory3D



• kernel, trasformation 3D






1.2.6 Integer 2D Geometric Contour

class name Integer 2D Geometric Contourcatalog name Gesture Processingcatalog id EywGPclass id IntGeometricContour






• EywGP, GeometricContour2DBase


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1.2.7 Integer 2D Geometric Trajectory

class name Integer 2D Geometric Trajectorycatalog name Gesture Processingcatalog id EywGPclass id IntGeometricTrajectory2D









1.2.8 Integer 2D Graphic Contour

class name Integer 2D Graphic Contourcatalog name Gesture Processingcatalog id EywGPclass id IntGraphicContour




• Base, Drawing



• EywGP, GraphicContour2DBase


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1.2.9 Integer 2D Graphic Trajectory

class name Integer 2D Graphic Trajectorycatalog name Gesture Processingcatalog id EywGPclass id IntGraphicTrajectory2D




• Base, Drawing



• EywGP, GraphicTrajectory2DBase



1.2.10 Integer 3D Geometric Trajectory

class name Integer 3D Geometric Trajectorycatalog name Gesture Processingcatalog id EywGPclass id IntGeometricTrajectory3D









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1.3 Datatype converters

1.3.1 Geometric To Graphic Trajectory Converter

class name Geometric To Graphic Trajectory Convertercatalog name Gesture Processingcatalog id EywGPclass id IntGeometricToGraphicTrajectory2DConverter

This converter converts a geometric trajectory to a graphic trajectory, i.e., a trajectorythat can be displayed.By default, the output graphic trajectory will be black and will havea thickness of one pixel.

1.3.2 Geometric To Graphic Trajectory Converter

class name Geometric To Graphic Trajectory Convertercatalog name Gesture Processingcatalog id EywGPclass id DoubleGeometricToGraphicTrajectory2DConverter

This converter converts a geometric trajectory to a graphic trajectory, i.e., a trajectorythat can be displayed.By default, the output graphic trajectory will be black and will havea thickness of one pixel.

1.3.3 Graphic To Geometric Trajectory Converter

class name Graphic To Geometric Trajectory Convertercatalog name Gesture Processingcatalog id EywGPclass id IntGraphicToGeometricTrajectory2DConverter

This converter converts a graphic trajectory to a geometric trajectory i.e., it removesgraphic information such as color and thickness.

1.3.4 Graphic To Geometric Trajectory Converter

class name Graphic To Geometric Trajectory Convertercatalog name Gesture Processingcatalog id EywGPclass id DoubleGraphicToGeometricTrajectory2DConverter

This converter converts a graphic trajectory to a geometric trajectory i.e., it removesgraphic information such as color and thickness.

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1.3.5 Trajectory To Dynamic Temporal Data Converter

class name Trajectory To Dynamic Temporal Data Convertercatalog name Gesture Processingcatalog id EywGPclass id DoubleGeomTraj2DToDynTempDataConverter

This converter converts a trajectory to a time series having as many channels as thenumber of componenets of the trajectory points and as many samples as the number ofpoints of the trajectory.


class name Trajectory To Dynamic Temporal Data Convertercatalog name Gesture Processingcatalog id EywGPclass id DoubleGraphicTraj2DToDynTempDataConverter



class name Trajectory To Dynamic Temporal Data Convertercatalog name Gesture Processingcatalog id EywGPclass id DoubleGeomTraj3DToDynTempDataConverter


1.4 Authors

1.4.1 Gualtiero Volpe

class name Gualtiero Volpecatalog name Gesture Processingcatalog id EywGPclass id gualtiero

Software Engineer mailto:[email protected]

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1.4.2 Paolo Coletta

class name Paolo Colettacatalog name Gesture Processingcatalog id EywGPclass id paolo

[email protected]

1.5 Companies

1.5.1 InfoMus Lab

class name InfoMus Labcatalog name Gesture Processingcatalog id EywGPclass id InfoMus

Laboratory of Musical Informatics http://www.infomus.dist.unige.it news://infomus.dist.unige.itmailto:[email protected]

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Chapter 2

Machine Learning Catalog

2.1 Blocks

2.1.1 LibSVM Save

bitmap

class name LibSVM Savecatalog name Machine Learningcatalog id EywMLclass id LibSVMSaveauthors Gualtiero Volpe

This block allows saving a matrix in the format required by the libsvm tools)

Inputs

Input Matrixid InputMatrixtype Kernel, Double matrix.type id kernel, double-matrix



The input matrix to be saved in LibSVM format

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Parameters

File Nameid FileNametype Kernel, String datatype (Kernel Catalog).type id kernel, string

layout

Filename,MustExist=true,SaveMode=true,OverwritePrompt=true,Filter=”Text files (*.txt)—*.txt—All files (*.*)—*.*——”

Name of the output file that will be formatted in LibSVM style

Save Modeid SaveModetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:AppendOverwrite

domain [ 0, 1 ]Specify whether new data should overwrite the older ones or if the new data are ap-

pended after the older ones

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2.1.2 SVM Predict

bitmap

class name SVM Predictcatalog name Machine Learningcatalog id EywMLclass id SVMauthors Gualtiero Volpe

This block provides support for performing prediction using Support Vector Machines(SVMs)

Inputs




Input data to be analysed by the SVM

Outputs

Output Vectorid OutputVectortype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

Output from the SVM model

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Parameters

Model File Nameid ModelFileNametype Kernel, String datatype (Kernel Catalog).type id kernel, string

layout

Filename,MustExist=true,SaveMode=false,OverwritePrompt=true,Filter=”Text files (*.txt)—*.txt—All files (*.*)—*.*——”

Name of the file including the SVM model

Refresh modelid RefreshModeltype Kernel, Trigger datatype (Kernel Catalog).type id kernel, trigger

Refresh the SVM model

Show probabilitiesid Probabilitiestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set, an additional output is generated, providing probabilties values for classes. Notethat the model should be suitably trained in order to provide probabilities values.

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2.1.3 SVM Train

bitmap

class name SVM Traincatalog name Machine Learningcatalog id EywMLclass id SVMTrainauthors Gualtiero Volpe

This block provides support for training a Support Vector Machine (SVM)

Inputs




The input training set

Outputs

Trainedid TrainedFlagtype Kernel, Int datatype (Kernel Catalog).type id kernel, intinplace id *no*inherited id *no*

The block returns a non-zero value if an SVM model has been successfully generated.

Rangesid Rangestype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

Ranges of the training set attributes

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Parameters

SVM Typeid SVMTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:C-SVCNu-SVCOne-class SVMEpsilon-SVRNu-SVR

domain [ 0, 4 ]Specify the type of SVM

Kernel Typeid KernelTypetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layout

Combo Box:LinearPolynomialRadial Basis FunctionSigmoid

domain [ 0, 3 ]Specify the type of the SVM kernel

Degreeid Degreetype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Degree of the kernel polynome (for polynomial SVMs only)

Gammaid Gammatype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Gamma value for the SVM model

Offsetid Coeff0type Kernel, Double datatype (Kernel Catalog).type id kernel, double

Offset value for the SVM model

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Epsilonid Epsilontype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Stopping criterium

Cache Sizeid CacheSizetype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Cache size in MB

Cid Ctype Kernel, Double datatype (Kernel Catalog).type id kernel, double

C value for the SVM model

Nuid Nutype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Nu value for the SVM model

pid ptype Kernel, Double datatype (Kernel Catalog).type id kernel, double

p value for the SVM model

Shrinkingid Shrinkingtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Use shrinking heuristics

Probabilityid Probabilitytype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

Do probability estimates

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Model File Nameid ModelFileNametype Kernel, String datatype (Kernel Catalog).type id kernel, string

layout


Name of the file containing the model

Train Modelid TrainModeltype Kernel, Trigger datatype (Kernel Catalog).type id kernel, trigger

Trains the Support Vector Machine

Training Modeid TrainingModetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

layoutCombo Box:IncrementalOne shot

domain [ 0, 1 ]Select whether to incremenatally update the model or to perform a one-shot training.

Enable Maximum Sizeid EnableMaxSizetype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, check for a maximum size of the training set. In case such maximum sizeis reached, training is forced.

Maximum size of training setid MaxSizetype Kernel, Int datatype (Kernel Catalog).type id kernel, int

The maximum allowed size for the training set.

Enable Loggingid EnableLogtype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, enable writing of a log file containing information on the training

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Log File Nameid LogFileNametype Kernel, String datatype (Kernel Catalog).type id kernel, string

layout


Name of the log file

Scale Valuesid ScaleValuestype Kernel, Bool datatype (Kernel Catalog).type id kernel, bool

If set to true, the block will scale the values in the training set

Lower Boundid LowerBoundtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Lower bound of the rescaled training set

Upper Boundid UpperBoundtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Upper bound of the rescaled training set

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2.1.4 Stabilizer

bitmap

class name Stabilizercatalog name Machine Learningcatalog id EywMLclass id Stabilizerauthors Gualtiero Volpe

This block stabilizes the output of a machine learning classifier.

Inputs

Input valueid InputValuetype Kernel, Double datatype (Kernel Catalog).type id kernel, double



Input value to the stabilizer

Outputs

Recognitionid Recognizedtype Kernel, Int datatype (Kernel Catalog).type id kernel, intinplace id *no*inherited id *no*

True if the current value is recognized

Output valueid OutputValuetype Kernel, Double datatype (Kernel Catalog).type id kernel, doubleinplace id *no*inherited id *no*

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Output value from the stabilizer

Parameters

Number of valuesid NValuestype Kernel, Int datatype (Kernel Catalog).type id kernel, intdomain [ 1, +infinity )

Number of previous values the stabilizer takes into account

Thresholdid Thresholdtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Threshold for the stabilizer.

Not recognized valueid NotRecValuetype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Value returned as output when the input is not recognized

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2.1.5 Vector Rescaler

bitmap

class name Vector Rescalercatalog name Machine Learningcatalog id EywMLclass id VectorRescalerauthors Gualtiero Volpe

This block rescales the input vector according to the given ranges.

Inputs

Input Vectorid InputVectortype Kernel, Double matrix.type id kernel, double-matrix



Input vector

Input Rangesid InputRangestype Kernel, Double matrix.type id kernel, double-matrix



Ranges of each item in the input vector

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Outputs

Output Vectorid OutputVectortype Kernel, Double matrix.type id kernel, double-matrixinplace id *no*inherited id *no*

Rescaled vector

Parameters

Lower boundid OutputLowBoundtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Lower bound of the rescaled vector

Upper boundid OutputHighBoundtype Kernel, Double datatype (Kernel Catalog).type id kernel, double

Upper bound of the rescaled vector

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2.2 Datatypes

2.3 Authors

2.3.1 Gualtiero Volpe

class name Gualtiero Volpecatalog name Machine Learningcatalog id EywMLclass id gualtiero

Software Engineer mailto:[email protected]

2.4 Companies

2.4.1 InfoMus Lab

class name InfoMus Labcatalog name Machine Learningcatalog id EywMLclass id InfoMus

InfoMus Lab Laboratory of Musical Informatics http://infomus.dist.unige.it news://infomus.dist.unige.itmailto:[email protected]

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Part III

Appendices

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Appendix A

Release notes

A.1 Notes on EyesWeb XMI Expressive Gesture pro-

cessing Library 5.0.3.0

Released on Xxxx XX, 2009.

• TODO Finally released

• Added block GeneralisedAutoCorrelation

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Appendix B

License

Use of the EyesWeb Gesture Processing Library (hereinafter ’SOFTWARE’) is contingenton your agreement to the following terms:

WARRANTY & USE: DIST - University of Genoa grants you a limited, non-exclusivelicense to use the SOFTWARE free of charge only for educational purposes and researchat universities and government research laboratories. Companies and private research lab-oratories are required to obtain a separate license. For any other (not educational) use,commercial or private, it is also required to obtain a license. DIST - University of Genoamakes no representations about the suitability of this software for any purpose. It is pro-vided ”as is” without express or implied warranty. DIST - University of Genoa is notobligated to provide maintenance or updates for the SOFTWARE.

DISTRIBUTION: universities and government research laboratories may freely dis-tribute the SOFTWARE, in original version, only to other universities and governmentresearch laboratories provided that this copyright and permission notice appear on all copiesand supporting documentation, and that the DIST - University of Genoa copyright noticesare referred in the following ways:

• DIST - University of Genoa’s copyright notice should be included in the documenta-tion, regardless of the media used to supply the documentation;

• The DIST - University of Genoa and EyesWeb Logos have to appear on packages andpromotional material (DIST - University of Genoa makes the logos available on theEyesWeb’s ftp site ftp://ftp.infomus.org;

• In the ’about box’ of the product, in the case that it is not the EyesWeb about box,DIST - University of Genoa and EyesWeb must be cited in the following manner:”EyesWeb is copyright (c) Laboratorio di Infomatica Musicale - DIST - Universityof Genoa (www.infomus.dist.unige.it)”, and the EyesWeb Gesture Processing Librarymust be cited in the following manner: ”The EyesWeb Gesture Processing Libraryis copyright (c) Laboratorio di Infomatica Musicale - DIST - University of Genoa(www.infomus.dist.unige.it)”.

• Any public event or product/applications must be communicated in advance to [email protected].

Distribution to companies, private research laboratories, or any other kind of subjectmust be preliminarly agreed with DIST.

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EyesWeb 5.0.3 Gesture Processing User Manual

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