ImageDev

TextureClassificationModel

Contains all the information necessary to run a trained machine learning model for performing a texture-based segmentation.

The object publicly exposes only the most relevant information of the model. Other important information is stored in this model, such as the classification weights, but is not accessible.

Information about the measurement named measurement can be accessed by using the method measurementInformation() which returns a FieldInformation
Information about the measurement named measurement can be accessed by using the property measurementInformation which returns a FieldInformation
Information about the measurement named measurement can be accessed by using the property measurement_information which returns a FieldInformation

Object members

Measurement name DescriptionElement typeIndexingPhysical Information
computeMode The type of dimensional image to process with the model (2D or 3D). String None UNKNOWN
coocRadius The radius, in pixels, of the circular neighborhood used by the co-occurrence features. Unsigned integer None UNKNOWN
coocTextonShape The shape of the co-occurrence texton; that is, the pattern defined by the set of co-occurrence vectors. String None UNKNOWN
listOfRadius The list of radius, in pixels, of the circular neighborhoods used for computing textural features. Unsigned integer [radiusIndex] UNKNOWN
numOfClasses The number of classes learned by the model. Unsigned integer None UNKNOWN
selectedFeature The list of features retained by the training to discrimate the textures. String [featureIndex] UNKNOWN
separationPower The parameter controlling the rejection criteria of the feature selection algorithm. Floating point None UNKNOWN
featureGroup The feature groups selected when initializing the model. String [radiusIndex] UNKNOWN

Object methods

Method Description
void toDataFrame() Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify the different elements. For instance:
  • index [label]: The index of the label. This index systematically starts from 0 and is the label value minus 1 when all label values are represented. When there are some missing label values, the corresponding label value cannot be directly deduced from this index.
  • index [time]: The sequence index of a time series or of an image stack.
Method Description
void ToDataFrame() Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify the different elements. For instance:
  • index [label]: The index of the label. This index systematically starts from 0 and is the label value minus 1 when all label values are represented. When there are some missing label values, the corresponding label value cannot be directly deduced from this index.
  • index [time]: The sequence index of a time series or of an image stack.
Method Description
void to_data_frame() Convert the measurement to an IOLink.DataFrame
One or more "index" columns will be added at the beginning of the dataframe to identify the different elements. For instance:
  • index [label]: The index of the label. This index systematically starts from 0 and is the label value minus 1 when all label values are represented. When there are some missing label values, the corresponding label value cannot be directly deduced from this index.
  • index [time]: The sequence index of a time series or of an image stack.

Related algorithms




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