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Diffstat (limited to 'src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg')
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1 files changed, 422 insertions, 0 deletions
diff --git a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg b/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg new file mode 100644 index 00000000..142470cf --- /dev/null +++ b/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg @@ -0,0 +1,422 @@ +#------------------------------------------------------------------------------ +# activedtw.cfg +# +# Configuration file for Active-DTW Classification Method for +# Lipi Toolkit 4.0.0 +#------------------------------------------------------------------------------ + +#------------------------------------------------------------------------------ +# The standard format for the configuration entries is the name of the +# configuration parameter seperated by an equal to sign and then the value of +# the configuration parameter. For example: +# ConfigurationEntryName = value +# +# Lines starting with a # are commnet lines +# +# A cfg entry is strictly a key value pair and leaving the key without the +# value or specification of a value out of the range is not permitted +# +# If a cfg entry is not specified at all, then default values are used by the +# recognizer +#------------------------------------------------------------------------------ + +#------------------------------- +# PREPROCESSING +#------------------------------- + +#------------------------------------------------------------------------------- +# ResampTraceDimension +# +# Description: The number of target points for resampling. In other words, +# each character will be resampled to this number of points. In case of +# multistroke characters, this number of points will be distributed between +# the strokes in proportion to their lengths in proportion to their initial +# number of points. +# +# Valid values: Any integer > 0 +# Units: Points +# Default value: 60 +# Typical value: Average number of points per character in the training data set. +#------------------------------------------------------------------------------- +ResampTraceDimension = 60 + + + +#------------------------------------------------------------------------------- +# ResampPointAllocation +# +# Description: Method to be used for point allocation among different strokes +# during resampling. Two schemes have been implemented lengthbased and point +# based. In lengthbased allocation scheme, the number of points allocated to +# each stroke is proportional to the length of the stroke. Length of a stroke +# is calculated as the sum of the distances between each point in the stroke. +# In the pointbased allocation scheme, the target stroke point allocation is +# proportional to the number of points in the initial stroke. +# +# Valid value: [lengthbased | pointbased] +# Default value: lengthbased +#------------------------------------------------------------------------------- +ResampPointAllocation = lengthbased + + +#------------------------------------------------------------------------------- +# NormDotSizeThreshold +# +# Description: This threshold is used to determine whether a character is a dot. +# It is expressed in real length terms (inches) and converted internally to +# points using knowledge of the device�s spatial resolution. If the width +# and height are both less than this threshold, then all the points are replaced +# with the center of the of the normalized character, basically to represent it +# as a dot +# +# Valid values: Any real number > 0 +# Units: inches +# Default value: 0.01 +# Typical value: < 0.1 +#------------------------------------------------------------------------------- +NormDotSizeThreshold = 0.01 + +#------------------------------------------------------------------------------- +# NormLineWidthThreshold +# +# Description: This threshold is used to detect whether the character is a +# vertical or horizontal line. If only the height is less than this threshold +# then the character is detected as a horizontal line and if only the width is +# less than this threshold then the character is detected as a vertical line. +# Assuming the height is along the y-dimension and width is along the x- +# dimension, during normalization of a horizontal line only the x-coordinates +# are scaled and the y-coordinates are translated to the center of the character, +# with out scaling. Similarly for the vertical line only the y-coordinates are +# normalized and the x-coordinates are translated to the center with out scaling +# +# Valid values: Any real number > 0 +# Units: inches +# Default value: 0.01 +# Typical value: < 0.1 +#------------------------------------------------------------------------------- +NormLineWidthThreshold = 0.01 + +#------------------------------------------------------------------------------- +# NormPreserveAspectRatio +# +# Description: This parameter is used to indicate whether the aspect ratio +# has to be preserved during normalization. The aspect ratio is the calculated +# as maximum of (height/width , width/height). The aspect ratio is preserved only +# if the calculated aspect ratio is greater than the threshold value specified +# through NormPreserveAspectRatioThreshold and this configuration variable is +# set to true. If this configuration variable is set to false the aspect ratio +# is not preserved during normalization. +# +# Valid value: [true | false] +# Default value: true +#------------------------------------------------------------------------------- +NormPreserveAspectRatio = true + + +#------------------------------------------------------------------------------- +# NormPreserveAspectRatioThreshold +# +# Description: Aspect ratio is preserved during normalization if the computed +# aspect ratio (max(height/width, width/height)) is greater than this threshold +# and the configuration value NormPreserveAspectRatio is set to true. During +# aspect ratio preserving normalization, the larger of the two dimensions is +# normalized to the standard size and the other dimension is normalized +# proportional to the initial height and width ratio, so that the initial +# aspect ratio is maintained. +# +# Valid values: Any real number >= 1 +# Default value: 3 +# Typical value: >= 1.5 +#------------------------------------------------------------------------------- +NormPreserveAspectRatioThreshold = 3 + +#------------------------------------------------------------------------------- +# NormPreserveRelativeYPosition +# +# Description: The relative Y position is the mean of the y-coordinates in the +# input character. During normalization if this parameter is set to true, each +# y-coordinate of the character point is translated by the initial y-mean value, +# so that the mean of the y-coordinates remains the same before and after +# normalization. This is typically used in the word recognition context where +# each stroke of the character has to be normalized separately and the relative +# position of the strokes should be maintained even after normalization. +# +# Valid value: [true | false] +# Default value: false +#------------------------------------------------------------------------------- +NormPreserveRelativeYPosition = false + +#------------------------------------------------------------------------------- +# SmoothWindowSize +# +# Description: The configuration value specifies the length of the moving +# average filter (size of the window) for smoothing the character image. +# If this value is set to N, then each point in the input character is replaced +# by the average of value of this point, (N-1)/2 points on the right and (N-1)/2 +# on the left of this point. +# +# Valid value: Any integer > 0 +# Units: Points +# Typical value: 5 +# Default value: 3 +#------------------------------------------------------------------------------- +SmoothWindowSize = 3 + +#------------------------------------------------------------------------------- +# NNPreprocSequence +# +# Description: This variable is used to specify the sequence of preprocessing +# operations to be carried out on the input character sample before extracting +# the features. A valid preprocessing sequence can consist of combination of one +# or more of the functions selected from the valid values set mentioned below. +# The CommonPreProc prefix is used specify the default preprocessing module of +# LipiTk. The user can add his own preprocessing functions in other modules and +# specify them in the preprocessing sequence. +# +# Valid values: Any sequence formed from the following set +# CommonPreProc::normalizeSize; +# CommonPreProc::removeDuplicatePoints; +# CommonPreProc::smoothenTraceGroup; +# CommonPreProc::dehookTraces; +# CommonPreProc::normalizeOrientation; +# CommonPreProc::resampleTraceGroup; +# Default value: {CommonPreProc::normalizeSize,CommonPreProc::resampleTraceGroup,CommonPreProc::normalizeSize} +#------------------------------------------------------------------------------- +PreprocSequence={CommonPreProc::normalizeSize,CommonPreProc::resampleTraceGroup,CommonPreProc::normalizeSize} + +#--------------------------------------- +# TRAINING +#--------------------------------------- + +#------------------------------------------------------------------------------- +# NNTrainPrototypeSelectionMethod +# +# Description: This is used to specify the prototype selection method to be used +# while training the shape recognizer. When set to hier-clustering, the +# prototypes are selected using hierarchical clustering method. +# +# Valid value: [hier-clustering] +# Default value: hier-clustering +#------------------------------------------------------------------------------- +NNTrainPrototypeSelectionMethod=hier-clustering + + +#------------------------------------------------------------------------------- +# NNTrainPrototypeReductionFactorPerClass +# +# Description: This config parameter is used only when the prototype selection +# is clustering. This config parameter is used to specify the amount of the +# initial prototypes to be excluded during prototype selection. +# Set it to automatic if the number of clusters is to be determined +# automatically. Set it to none if no prototype selection is required. If the +# value of this parameter is set to a number between 1-100, say 25, then 75% +# (i.e 100-25) of the initial training data are retained as prototypes. +# This parameter can be specified only if the NNTrainNumPrototypesPerClass +# is not specified. +# +# Valid value: [automatic | none | any real number from 0-100] +# Default value: automatic +#------------------------------------------------------------------------------- +NNTrainPrototypeReductionFactorPerClass = 25 + +#------------------------------------------------------------------------------- +# NNTrainNumPrototypesPerClass +# +# Description: This config parameter is used only when the prototype selection +# is clustering. This is used to specify the number of prototypes to be selected +# from the training data. This parameter can be specified only if +# PrototypeReductionFactor is not specified. This config entry is commented as +# only one of NNTrainPrototypeReductionFactorPerClass or +# NNTrainNumPrototypesPerClass can be active in a valid cfg file. +# +# Valid value: [automatic | none | any integer from 1-N] +# (N is the number of samples # per class) +# Default value: automatic +#------------------------------------------------------------------------------- +#NNTrainNumPrototypesPerClass=100 + +# Note: Only one of either PrototypeReductionFactor or NumClusters can be +# enabled at any particular instance + +#------------------------------------------------------------------------------- +# ActiveDTWRetainPercentEigenEnergy +# +# Description: This config parameter is used to specify the amount of Eigen +# energy to be included to select the number of eigen vectors +# +# Valid value: [any integer from 0-100] +# +# Default value: 90 +#------------------------------------------------------------------------------- +ActiveDTWRetainPercentEigenEnergy= 90 + +#------------------------------------------------------------------------------- +# ActiveDTWMinClusterSize +# +# Description: This config parameter is used to specify the minimum number +# of samples required to form a cluster +# +# Valid value: [any postive integer > 1] +# +# Default value: 2 +#------------------------------------------------------------------------------- +ActiveDTWMinClusterSize = 2 + +#----------------------------------------- +# FEATURE EXTRACTION +#----------------------------------------- + +#------------------------------------------------------------------------------- +# FeatureExtractor +# +# Description: The configuration value is used to specify the feature extraction +# module to be used for feature extraction. The point float feature extraction +# module extracts the x,y,cosine and sine angle features at every point of the +# character. +# +# Valid value: [PointFloatShapeFeatureExtractor] +# Default value: PointFloatShapeFeatureExtractor +#------------------------------------------------------------------------------- +FeatureExtractor=PointFloatShapeFeatureExtractor + +#----------------------------------------- +# RECOGNITION +#----------------------------------------- + +#------------------------------------------------------------------------------- +# NNRecoDTWEuFilterOutputSize +# +# Description: This config parameter is used to set the proportion of nearest +# cluster or singleton vectors from a class (filtered based on euclidean distance) +# to be considered for calculating deformations or dtw distance. Set to 100 if +# all clusters or singletons are to be considered for calculating dtw distance. +# This is mainly used to increase the speed of recognition. +# +# Valid value: [all | any number from 1-100] +# Default Value: all +#------------------------------------------------------------------------------- +NNRecoDTWEuFilterOutputSize = 30 + +#------------------------------------------------------------------------------- +# ActiveDTWEigenSpreadValue +# +# Description: This value is used to configure the range of values the +# bound constraint optimization algorithm will take to calculate the +# optimal deformation sample +# Valid value: [greater than 0| default = 16] +#------------------------------------------------------------------------------- +ActiveDTWEigenSpreadValue = 16 + +#------------------------------------------------------------------------------- +# ActiveDTWUseSingleton +# +# Description: This value is used to configure whether singleton vectors +# from classes will be taken into consideration during the recognition +# process +# Valid value: [true | false] +# Default Value: true +#------------------------------------------------------------------------------- +ActiveDTWUseSingleton = true + +#------------------------------------------------------------------------------- +# NNRecoRejectThreshold +# +# Description: Threshold to reject the test sample. If the confidence obtained +# for the recognition of test sample is less than this threshold then the test +# sample is rejected. +# +# Valid value: Any real number from 0-1 +# Default value: 0.001 +#------------------------------------------------------------------------------- +NNRecoRejectThreshold = 0.001 + +#------------------------------------------------------------------------------- +# NNRecoNumNearestNeighbors +# +# Description: Number of nearest neighbors to be considered during recognition +# and computation of confidence. If the value is set to 1, nearest neighbor +# classifier is used, otherwise k-nearest neighbor or Adaptive k-nearest +# neighbor classifiers are used. By default, nearest neighbor classifier is used. +# +# Valid value: Any integer >= 1 +# Default value: 1 +#------------------------------------------------------------------------------- +NNRecoNumNearestNeighbors = 1 + +#------------------------------------------------------------------------------- +# NNRecoUseAdaptiveKNN +# +# Description: This parameter is used to specify whether Adaptive k-nearest +# neighbor recognizer (A-kNN) is to be used. If set to true, A-kNN recognizer is +# used, otherwise kNN recognizer is used. The A-kNN recognizer automatically +# determines the number of nearest neighbors to be considered for recognition in +# each class. If NNRecoNumNearestNeighbors is set to 1, this parameter is +# automatically set to false and the manually set value will not be considered. +# +# Valid value: [true | false] +# Default value: false +#------------------------------------------------------------------------------- +NNRecoUseAdaptiveKNN = false + +#-------------------------------------------- +# ADAPTATION +#-------------------------------------------- + +#------------------------------------------------------------------------------- +# ActiveDTWMaxClusterSize +# +# Description: This config parameter is used to specify the maximum number +# of samples a cluster is permitted to have +# +# Valid value: [any postive integer > 1 And Greater than ActiveDTWMinClusterSize] +# +# Default value: 2 +#------------------------------------------------------------------------------- +ActiveDTWMaxClusterSize = 30 + +#-------------------------------------------- +# COMMON FOR TRAINING AND RECOGNITION +#-------------------------------------------- + + +#------------------------------------------------------------------------------- +# NNDTWBandingRadius +# +# Description: This configuration parameter specifies the banding radius +# to be used for DTW computation. This is used to speed up the computation +# process. If this value is zero no banding is done. The value is specified as +# fraction of ResampTraceDimension to be used while computing the DTW +# distance. +# +# Valid values: Any real number > 0 and <= 1 +# Default Value: 0.33 +#------------------------------------------------------------------------------- +NNDTWBandingRadius=0.33 + +#------------------------------------------------------------------------------- +#ActiveDTWMDTFileUpdateFreq +# +# Description: This configuration parameter specifies the number of iterations after +# which MDT file is to be updated. +# Every call to addClass or deleteClass will add/delete the given class. These +# in-memory changes will be reflected in nn.mdt only after the specified +# number of such iterations and on application exit. +# +# Valid values: Any integer > 0 +# Default value: 5 +# Typical value: 5 +#------------------------------------------------------------------------------- +ActiveDTWMDTFileUpdateFreq = 100 + +#------------------------------------------------------------------------------- +# NNMDTFileOpenMode +# +# Description: This configuration parameter specifies the mode for +# opening the mdt file. +# +# Valid values: ascii, binary +# Default Value: ascii +#------------------------------------------------------------------------------- + +NNMDTFileOpenMode=ascii + |