Proposals:Refactoring Statistics Framework 2007 Migration Users Guide: Difference between revisions
(→Algorithms: note change in Algorithm namespace.) |
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| Histogram || Refactored same name || | | Histogram || Refactored same name || | ||
* Modified by removing the measurement vector size from its template parameters. This Histogram class can now be used for Samples whose measurement vector size is not known at compilation time. The consequence is that now users MUST remember to set the measurement vector size before they call the Initialize() method, otherwise, an Exception will be thrown. | |||
* GetIndex() now take the InstanceIdentifier by copy, not as a const reference. | * GetIndex() now take the InstanceIdentifier by copy, not as a const reference. | ||
* Size() doesn't return unsigned int anymore. Instead it returns InstanceIdentifier (64 bits in mind) | * Size() doesn't return unsigned int anymore. Instead it returns InstanceIdentifier (64 bits in mind) | ||
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*** This method is quite unsafe, and doesn't provide any required functionality (it seems to be just a sign of feature-creep) | *** This method is quite unsafe, and doesn't provide any required functionality (it seems to be just a sign of feature-creep) | ||
|- | |- | ||
| VariableDimensionHistogram || | | VariableDimensionHistogram || Merged with the Histogram class above || || | ||
|- | |- | ||
| KdTree || | | KdTree || moved with the same name || || | ||
|- | |- | ||
|} | |} | ||
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The above frequency types are defined in the MeasurmentVectorTraits as follows | The above frequency types are defined in the MeasurmentVectorTraits as follows <br> | ||
<font style="color: red;"> | |||
/** In the old framework, the FrequencyType is set to float. The problem is for large histograms the total frequency can be more than 1e+7, than increasing the frequency by one does not change the total frequency (because of lack of precision). Using double type will also ultimately fall into the same problem. Hence in the new statistics framework, InstanceIdentifier/FrequencyTypes are set to the the largest possible integer on the machine */ <br> | |||
</font> | |||
typedef std::vector<int>::size_type InstanceIdentifier; <br> | typedef std::vector<int>::size_type InstanceIdentifier; <br> | ||
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|} | |} | ||
== Traits == | == Traits == | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| ImageToCoocurrenceListAdaptor || | | ImageToCoocurrenceListAdaptor || Refactored into "ScalarImageToCoocurrenceListSampleFilter" || | ||
* Compute() method removed ( Update() ) should be used instead | |||
* SetImage() method removed ( SetInput() ) should be used instead | |||
|| | |||
* Derives from Process Object and integrated into the pipeline | |||
** SetInput() method added | |||
** User should invoke Update() to generate the coocurrence list sample | |||
** GetOutput() returns the list sample generated | |||
|- | |- | ||
| ImageToListAdaptor || Refactored into "ImageToListSampleAdaptor". This class handles images with scalar, fixed size array (itk::FixedArray) and variable length vector (itk::VariableLengthVector) pixel types. | | ImageToListAdaptor || Refactored into "ImageToListSampleAdaptor". This class handles images with scalar, fixed size array (itk::FixedArray) and variable length vector (itk::VariableLengthVector) pixel types. | ||
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|| | || | ||
* The class derives from ListSample with the measurement vector type determined from the point set type. | * The class derives from ListSample with the measurement vector type determined from the point set type. | ||
* GetPointSet() returns a const PointSet ( constant correctness | * GetPointSet() returns a const PointSet ( constant correctness | ||
* GetSize() returns InstanceIdentifer type ( to have a consistent signature with the superclass ) | * GetSize() returns InstanceIdentifer type ( to have a consistent signature with the superclass ) | ||
* The Iterator and ConstIterator API is modified to be consistent with the iterator and constiterators of the ListSample class. | * The Iterator and ConstIterator API is modified to be consistent with the iterator and constiterators of the ListSample class. | ||
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== Algorithms == | == Algorithms == | ||
Algorithms, including FloorLog, MedianOfThree, FindSampleBound, FindSampleBoundAndMean, Partition, QuickSelect, NthElement, InsertSort, DownHeap, HeapSort, IntrospectiveSortLoop, IntrospectiveSort, have been '''moved''' from the '''itk::Statistics''' namespace to the '''itk::Statistics::Algorithm''' namespace. | |||
{| border="1" | {| border="1" | ||
|- bgcolor="#abcdef" | |- bgcolor="#abcdef" | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| MeanCalculator || Refactored into | | MeanCalculator || Refactored into MeanSampleFilter || | ||
* SetSample() method replaced with SetInput() | * SetSample() method replaced with SetInput() | ||
* GetOutput() method return type modified ( the new signature returns a data object decorator ) | * GetOutput() method return type modified ( the new signature returns a data object decorator ) | ||
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* GetMean() (returns the mean vector as it is without any decoration ) | * GetMean() (returns the mean vector as it is without any decoration ) | ||
|- | |- | ||
| WeightedMeanCalculator || Refactored into | | WeightedMeanCalculator || Refactored into WeightedMeanSampleFilter || | ||
* SetSample() method replaced with SetInput() | * SetSample() method replaced with SetInput() | ||
* SetWeights() method input argument modified ( the new signature takes dataobject decorated weight array) | * SetWeights() method input argument modified ( the new signature takes dataobject decorated weight array) | ||
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** SetWeightingFunction (Takes dataObject decorated version of weighting function type ) | ** SetWeightingFunction (Takes dataObject decorated version of weighting function type ) | ||
|- | |- | ||
| CovarianceCalculator || Refactored into | | CovarianceCalculator || Refactored into CovarianceSampleFilter || | ||
* SetMean() removed ( the mean will always be computed inside the class. Keeping track of mean computed internally and set from outside creates lots of inconsistencies in the pipeline). | * SetMean() removed ( the mean will always be computed inside the class. Keeping track of mean computed internally and set from outside creates lots of inconsistencies in the pipeline). | ||
* SetSample() replaced with SetInput() | * SetSample() replaced with SetInput() | ||
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** GetCovarianceMatrixOutput() ( returns a decorated version of the covariance matrix ) | ** GetCovarianceMatrixOutput() ( returns a decorated version of the covariance matrix ) | ||
|- | |- | ||
| WeightedCovarianceCalculator || Refactored into | | WeightedCovarianceCalculator || Refactored into WeightedCovarianceSampleFilter || | ||
* SetSample() method replaced with SetInput() | * SetSample() method replaced with SetInput() | ||
* SetWeights() method input argument modified ( the new signature takes dataobject decorated weight array) | * SetWeights() method input argument modified ( the new signature takes dataobject decorated weight array) | ||
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|- | |- | ||
| ScalarImageTextureCalculator || | | ScalarImageTextureCalculator || Refactored into ScalarImageToTextureFeaturesFilter || | ||
* Compute() removed ( Update() method should be used in the new API ) | |||
|| | |||
* Derives from ProcessObject and the following methods added to integrate it to the pipeline | |||
** GenerateData() ( user should invoke Update() to compute the features ) | |||
** Decorated data types for the mean and standard deviation feature values were defined. | |||
** Data object decorated versions of the GetOutput methods for the mean and standard deviation were added. | |||
*** GetFeatureMeansOutput() | |||
*** GetStandardDeviationsOutput() | |||
* GetInput and GetMaskImage() methods were added | |||
|} | |} | ||
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|- | |- | ||
| MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator || Deprecated ( Use ScalarImageToCooccurrenceMatrixFilter instead since this takes a mask image in addition to the input image ) || || | | MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator || Deprecated ( Use ScalarImageToCooccurrenceMatrixFilter instead since this takes a mask image in addition to the input image ) || || | ||
|- | |||
| ImageToHistogramGenerator || Refactored into ImageToHistogramFilter || | |||
* It used to derive from Object, now derives from ProcessObject | |||
* It used to expect a ListSample as input, it now takes any class that complies with the Sample API | |||
* Compute() implementation removed, the default Update() of the superclass ProcessObject() provides the correct functionality. Compute() was replaced with GenerateData(). | |||
* Member variables removed | |||
|| | |||
* It derives now from ProcessObject | |||
* SetInput( const SampleType * ) | |||
* const SampleType * GetInput() const | |||
* const HistogramType * GetOutput() const | |||
* Copy Constructor and Operator=() are declared protected are purposely not implemented in order to enforce the correct use of SmartPointers. | |||
* MakeOutput() method added, so that the filter is compatible with the DisconnectPipeline() API of the output DataObject. | |||
* Added DataDecorated Inputs for | |||
** Histogram Size | |||
** AutoMinimumMaximum | |||
** MarginalScale | |||
** HistogramBinMinimum | |||
** HistogramBinMaximum | |||
* Decorated inputs provide | |||
** Set/Get methods for the decorated type and | |||
** '''ONLY''' Set method for the undecorated type. | |||
** Get method for undecorated type cannot be provided because their API uses a const reference, and the decorated input may have been a NULL pointer, in which case there is not possible const reference to return. | |||
|} | |} | ||
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** '''ONLY''' Set method for the undecorated type. | ** '''ONLY''' Set method for the undecorated type. | ||
** Get method for undecorated type cannot be provided because their API uses a const reference, and the decorated input may have been a NULL pointer, in which case there is not possible const reference to return. | ** Get method for undecorated type cannot be provided because their API uses a const reference, and the decorated input may have been a NULL pointer, in which case there is not possible const reference to return. | ||
|- | |||
| HistogramToImageFilter || HistogramToImageFilter || || ImageDimension was added as second template parameter. This is needed because now the number of components in the Histogram class are defined at run time. | |||
|- | |||
| HistogramToIntensityImageFilter || HistogramToIntensityImageFilter || || ImageDimension was added as second template parameter. See HistogramToImageFilter | |||
|- | |||
| HistogramToLogProbabilityImageFilter || HistogramToLogProbabilityImageFilter || || ImageDimension was added as second template parameter. See HistogramToImageFilter | |||
|- | |||
| HistogramToProbabilityImageFilter || HistogramToProbabilityImageFilter || || ImageDimension was added as second template parameter. See HistogramToImageFilter | |||
|- | |||
| HistogramToEntropyImageFilter || HistogramToEntropyImageFilter || || ImageDimension was added as second template parameter. See HistogramToImageFilter | |||
|} | |} | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| || | | DensityFunction || Removed ( it is a duplication of what MembershipFunction) does || || | ||
|- | |||
| GaussianDensityFunction || Refactored as GaussianMembershipFunction || || | |||
|} | |} | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| || || || | | DistanceMetric || Refactored (Same name ) || | ||
*Evaluate method with two arguments removed | |||
|| | |||
*Derives now from FunctionBase ( not MembershipFunctionBase ) | |||
*SetMeasurmentVectorSize reimplemented | |||
|- | |||
| EuclideanDistance || Refactored as EuclideanDistanceMetric || | |||
*Evaluate method with two arguments removed (unnecessary for the classification pipeline ) | |||
*IsWithinRange method removed (unnecessary for the classification pipeline ) | |||
|| | |||
|- | |||
| MahalanobisDistanceMetric || Refactored same name || || | |||
|- | |||
|- | |||
| EuclideanSquareDistanceMetric|| New class || || Same API as EuclideanDistance | |||
|- | |||
| ManhattanDistanceMetric|| New class || || Same API as EuclideanDistance | |||
|} | |||
== MembershipFunctions == | |||
{| border="1" | |||
|- bgcolor="#abcdef" | |||
! Class Name !! Fate !! Deprecated API !! New API | |||
|- | |||
| DistanceToCentroidMembershipFunction || Refactored (Same name ) || | |||
*Set/Get Number of samples method removed ( not used at all ) | |||
|| | |||
*SetMeasurmentVectorSize reimplemented | |||
|- | |||
| GaussianDensityFunction || Refactored as GaussianMembershipFunction || | |||
|| | |||
|- | |||
|} | |||
== Process Objects == | |||
{| border="1" | |||
|- bgcolor="#abcdef" | |||
! Class Name !! Fate !! Deprecated API !! New API | |||
|- | |||
| SampleClassifier || These are now Classifiers, see below || || | |||
|- | |||
| SampleClassifierWithMask || These are now Classifiers, see below || || | |||
|} | |||
== Classifiers == | |||
{| border="1" | |||
|- bgcolor="#abcdef" | |||
! Class Name !! Fate !! Deprecated API !! New API | |||
|- | |||
| ClassifierBase || Deprecated || || | |||
|- | |||
| SampleClassifier || Refactored (as SampleClassifierFilter) || || | |||
* Derives from ProcessObject to integrate the classifier to ITK pipeline | |||
* Add Datadecorated inputs | |||
** Classlabels | |||
** MembershipFunctions | |||
* Throw an exception if the specified number of class doesn't match with | |||
the number of class labels or the number of membership functions specified. | |||
* Add Set/Get method for DecisionRule | |||
|- | |||
| ImageClassifierBase || Refactored (as ImageClassifierFilter) || || | |||
* Derives from ImageToImageFilter to integrate the classifier to ITK pipeline | |||
* Add Datadecorated inputs | |||
** Classlabels | |||
** MembershipFunctions | |||
** MembershipFunctionsWeightArray | |||
** DecisionRule | |||
* Throw an exception if the specified number of class doesn't match with | |||
the number of class labels or the number of membership functions specified. | |||
* Add Set/Get method for DecisionRule | |||
* GetOutput returns MembershipSampleType | |||
|- | |||
* SetSample becomes SetInput | |||
|} | |||
== Decision rules == | |||
{| border="1" | |||
|- bgcolor="#abcdef" | |||
! Class Name !! Fate !! Deprecated API !! New API | |||
|- | |||
| DecisionRuleBase || Refactored as DecisionRule || * Remove Evaluate(ArrayType ) method || | |||
* Define MembershipVector type which is of std::vector< double > type and store the membership score | |||
* Define only ONE Evaluate method that takes MembershipVector type | |||
|- | |||
| MaximumDecisionRule || Refactored as MaximumDecisionRule2 || || | |||
|- | |||
| MinimumDecisionRule || Refactored as MinimumDecisionRule2 || || | |||
|- | |||
|- | |||
| MaximumDecisionRatioRule || Refactored as MaximumDecisionRationRule2 || || | |||
|} | |} | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| || | | itkMixtureModelComponentBase || Refactored(same name) || | ||
* WeightsArrayType * GetWeights() non-const | |||
* double GetWeight( int ) non-const | |||
* void SetWeight( int ) | |||
* void CreateWeightArray() | |||
* void DeleteWeightArray() | |||
|| | |||
* const WeightsArrayType & GetWeights() const | |||
* double GetWeight( unsigned int ) const | |||
* void SetWeight( unsigned int ) | |||
* Removed (not needed now that Weights is not a pointer) | |||
* Removed (not needed now that Weights is not a pointer) | |||
|- | |||
| itkGaussianMixtureModelComponent || Refactored(same name) || || | |||
|} | |} | ||
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! Class Name !! Fate !! Deprecated API !! New API | ! Class Name !! Fate !! Deprecated API !! New API | ||
|- | |- | ||
| || | |itkKdTreeBasedKmeansEstimator || Refactored (same name) || || | ||
|- | |||
|itkExpectationMaximizationMixtureModelEstimator || Refactored (same name) || || | |||
|} | |} |
Latest revision as of 07:06, 20 December 2010
How To Migrate your code to the New Statistics Framework
Measurement Containers
Class Name | Fate | Deprecated API | New API |
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Sample | Refactored |
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Subsample | Refactored |
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ListSampleBase | Deprecated. The class was unecessary, it was providing only a Search() method that should in fact be provided by a process class, not a data container. |
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ListSample | Refactored |
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MembershipSample | Refactoring in Progress Luis |
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Histogram | Refactored same name |
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VariableDimensionHistogram | Merged with the Histogram class above | ||
KdTree | moved with the same name |
Frequency Containers
The use of the data type "FrequencyType" defined in the frequency containers was very confusing in the previous statistics framework. FrequencyType was used to refer to the number of counts(hits) in a histogram bin. This is basically what absolute frequency is. In other instances, FrequencyType was used to refer to relative (normalized) frequencies. This concept mixing creates a lot of confusion. Hence, in the new statistics framework, we have separated the two concepts and defined separate types.
//Absolute Frequency type alias
typedef MeasurementVectorTraits::AbsoluteFrequencyType AbsoluteFrequencyType;
//Relative Frequency type alias
typedef MeasurementVectorTraits::RelativeFrequencyType RelativeFrequencyType;
Formal definition of relative and absolute frequencies can be found at
http://en.wikipedia.org/wiki/Frequency_(statistics)
The above frequency types are defined in the MeasurmentVectorTraits as follows
/** In the old framework, the FrequencyType is set to float. The problem is for large histograms the total frequency can be more than 1e+7, than increasing the frequency by one does not change the total frequency (because of lack of precision). Using double type will also ultimately fall into the same problem. Hence in the new statistics framework, InstanceIdentifier/FrequencyTypes are set to the the largest possible integer on the machine */
typedef std::vector<int>::size_type InstanceIdentifier;
/** Type defined for representing the frequency of measurement vectors */
typedef InstanceIdentifier AbsoluteFrequencyType;
typedef NumericTraits< AbsoluteFrequencyType >::RealType RelativeFrequencyType;
typedef NumericTraits< AbsoluteFrequencyType >::AccumulateType TotalAbsoluteFrequencyType;
typedef NumericTraits< RelativeFrequencyType >::AccumulateType TotalRelativeFrequencyType;
The containers are renamed with a suffix "2" to be able to generate a separate ITKStatistics library and be able to link with the old library during the refactoring. But once the refactoring is finialized, the name should be changed back to the original names.
Class Name | Fate | Deprecated API | New API |
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DenseFrequencyContainer | Refactored into DenseFrequencyContainer2 |
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SparseFrequencyContainer | Refactored into SpareFrequencyContainer2 |
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Traits
Class Name | Fate | Deprecated API | New API |
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MeasurementVectorTraits | Refactored (same name) |
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MeanShift
Class Name | Fate | Deprecated API | New API |
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Adaptors
Class Name | Fate | Deprecated API | New API |
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ImageToCoocurrenceListAdaptor | Refactored into "ScalarImageToCoocurrenceListSampleFilter" |
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ImageToListAdaptor | Refactored into "ImageToListSampleAdaptor". This class handles images with scalar, fixed size array (itk::FixedArray) and variable length vector (itk::VariableLengthVector) pixel types. |
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ScalarImageToListAdaptor | Deprecated ( This class is redundant as the new ImageToListSampleAdaptor also handles scalar images with scalar pixel type. ) | ||
JointDomainImageToListAdaptor | Refactored into "JointDomainImageToListSampleAdaptor" |
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PointSetToListAdapor | Refactored into "PointSetToListSampleAdaptor" |
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Univariate Distributions
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
Algorithms
Algorithms, including FloorLog, MedianOfThree, FindSampleBound, FindSampleBoundAndMean, Partition, QuickSelect, NthElement, InsertSort, DownHeap, HeapSort, IntrospectiveSortLoop, IntrospectiveSort, have been moved from the itk::Statistics namespace to the itk::Statistics::Algorithm namespace.
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
Calculators
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
MeanCalculator | Refactored into MeanSampleFilter |
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WeightedMeanCalculator | Refactored into WeightedMeanSampleFilter |
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CovarianceCalculator | Refactored into CovarianceSampleFilter |
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WeightedCovarianceCalculator | Refactored into WeightedCovarianceSampleFilter |
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GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator | Refactored into HistogramToTextureFeaturesFilter |
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ScalarImageTextureCalculator | Refactored into ScalarImageToTextureFeaturesFilter |
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Generators
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
ListSampleToHistogramGenerator | Refactored into SampleToHistogramFilter |
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ImageToListGenerator | Refactored into ImageToListSampleFilter |
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ScalarImageToGreyLevelCooccurrenceMatrixGenerator | Refactored into ScalarImageToCooccurrenceMatrixFilter |
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MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator | Deprecated ( Use ScalarImageToCooccurrenceMatrixFilter instead since this takes a mask image in addition to the input image ) | ||
ImageToHistogramGenerator | Refactored into ImageToHistogramFilter |
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Filters
Class Name | Fate | Deprecated API | New API |
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ListSampleToHistogramFilter | Refactored as SampleToHistogramFilter |
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HistogramToImageFilter | HistogramToImageFilter | ImageDimension was added as second template parameter. This is needed because now the number of components in the Histogram class are defined at run time. | |
HistogramToIntensityImageFilter | HistogramToIntensityImageFilter | ImageDimension was added as second template parameter. See HistogramToImageFilter | |
HistogramToLogProbabilityImageFilter | HistogramToLogProbabilityImageFilter | ImageDimension was added as second template parameter. See HistogramToImageFilter | |
HistogramToProbabilityImageFilter | HistogramToProbabilityImageFilter | ImageDimension was added as second template parameter. See HistogramToImageFilter | |
HistogramToEntropyImageFilter | HistogramToEntropyImageFilter | ImageDimension was added as second template parameter. See HistogramToImageFilter |
Multivariate Density Functions
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
DensityFunction | Removed ( it is a duplication of what MembershipFunction) does | ||
GaussianDensityFunction | Refactored as GaussianMembershipFunction |
Distance Metrics
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
DistanceMetric | Refactored (Same name ) |
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EuclideanDistance | Refactored as EuclideanDistanceMetric |
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MahalanobisDistanceMetric | Refactored same name | ||
EuclideanSquareDistanceMetric | New class | Same API as EuclideanDistance | |
ManhattanDistanceMetric | New class | Same API as EuclideanDistance |
MembershipFunctions
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
DistanceToCentroidMembershipFunction | Refactored (Same name ) |
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GaussianDensityFunction | Refactored as GaussianMembershipFunction |
Process Objects
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
SampleClassifier | These are now Classifiers, see below | ||
SampleClassifierWithMask | These are now Classifiers, see below |
Classifiers
- SetSample becomes SetInput
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
ClassifierBase | Deprecated | ||
SampleClassifier | Refactored (as SampleClassifierFilter) |
the number of class labels or the number of membership functions specified.
| |
ImageClassifierBase | Refactored (as ImageClassifierFilter) |
the number of class labels or the number of membership functions specified.
|
Decision rules
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
DecisionRuleBase | Refactored as DecisionRule | * Remove Evaluate(ArrayType ) method |
|
MaximumDecisionRule | Refactored as MaximumDecisionRule2 | ||
MinimumDecisionRule | Refactored as MinimumDecisionRule2 | ||
MaximumDecisionRatioRule | Refactored as MaximumDecisionRationRule2 |
Components
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
itkMixtureModelComponentBase | Refactored(same name) |
|
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itkGaussianMixtureModelComponent | Refactored(same name) |
Estimators
Class Name | Fate | Deprecated API | New API |
---|---|---|---|
itkKdTreeBasedKmeansEstimator | Refactored (same name) | ||
itkExpectationMaximizationMixtureModelEstimator | Refactored (same name) |