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Diffstat (limited to 'src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg')
| -rw-r--r-- | src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg | 422 |
1 files changed, 0 insertions, 422 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 deleted file mode 100644 index 142470cf..00000000 --- a/src/plugins/lipi-toolkit/3rdparty/lipi-toolkit/src/reco/shaperec/activedtw/activedtw.cfg +++ /dev/null @@ -1,422 +0,0 @@ -#------------------------------------------------------------------------------ -# 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 - |