SoAutoThresholdingQuantification Class |
OIV.ImageViz.Engines.ImageAnalysis.Statistics.SoAutoThresholdingQuantification engine.
Namespace: OIV.ImageViz.Engines.ImageAnalysis.Statistics
The SoAutoThresholdingQuantification type exposes the following members.
Name | Description | |
---|---|---|
SoAutoThresholdingQuantification | Constructor. |
Name | Description | |
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AbortEvaluate | Abort current processing as soon as possible. | |
Copy | Creates and returns an exact copy of the engine. | |
CopyFieldValues(SoFieldContainer) | Calls CopyFieldValues(fc, false). (Inherited from SoFieldContainer.) | |
CopyFieldValues(SoFieldContainer, Boolean) | Copies the contents of fc's fields into this object's fields. | |
Dispose |
Releases all resources used by SoDisposable.
(Inherited from SoDisposable.) | |
EnableNotify | Notification at this Field Container is enabled (if flag == true) or disabled (if flag == false). | |
Equals | Determines whether the specified Object is equal to the current Object. (Inherited from Object.) | |
FieldsAreEqual | Returns true if this object's fields are exactly equal to fc's fields. | |
Get | Returns the values of the fields of this object in the Open Inventor ASCII file format in the given string. | |
GetAllFields | Returns a list of fields, including the eventIn's and eventOut's. | |
GetEventIn | Returns a the eventIn with the given name. | |
GetEventOut | Returns the eventOut with the given name. | |
GetField | Returns a the field of this object whose name is fieldName. | |
GetFieldName | Returns the name of the given field in the fieldName argument. | |
GetFields | Appends references to all of this object's fields to resultList, and returns the number of fields appended. | |
GetHashCode |
Overrides GetHashCode().
(Inherited from SoNetBase.) | |
GetName | Returns the name of an instance. | |
GetOutput | Returns a reference to the engine output with the given name. | |
GetOutputName | Returns (in outputName) the name of the engine output (output). | |
GetOutputs | Returns a list of outputs in this engine. | |
GetStringName | (Inherited from SoBase.) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
HasDefaultValues | Returns true if all of the object's fields have their default values. | |
IsEvaluating | Returns true if the engine evaluation is in progress. | |
IsNotifyEnabled | Notification is the process of telling interested objects that this object has changed. | |
IsSynchronizable | Gets the ScaleViz synchronizable state of this object. | |
Set | Sets one or more fields in this object to the values specified in the given string, which should be a string in the Open Inventor file format. | |
SetName | (Inherited from SoBase.) | |
SetSynchronizable | Sets this to be a ScaleViz synchronizable object. | |
SetToDefaults | Sets all fields in this object to their default values. | |
StartEvaluate | Evaluate engine and dependencies in another thread without blocking the current one. | |
ToString |
Converts this SoBase structure to a human readable string.
(Inherited from SoBase.) | |
Touch | Marks an instance as modified, simulating a change to it. | |
WaitEvaluate | Wait for the end of engine evaluation. |
Name | Description | |
---|---|---|
computeMode | Select the compute Mode (2D or 3D or AUTO) . | |
inGrayImage | The input grayscale image Default value is NULL. | |
intensityRangeInput | The input intensity range used when OIV.ImageViz.Engines.ImageAnalysis.Statistics.SoAutoThresholdingQuantification.rangeMode = OTHER. | |
IsDisposable | ISafeDisposable interface implementation.
(Inherited from SoDisposable.) | |
outResult | The thresholding results. | |
rangeMode | The input intensity range. | |
thresholdCriterion | The criterion to detect thresholds on histogram. | |
UserData |
Gets or sets the user data to be contained by the field container.
(Inherited from SoFieldContainer.) |
Name | Description | |
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OnBegin | Event raised when the processing begins. | |
OnEnd | Event raised when processing ends and the result is available. | |
OnProgress | Event raised while processing is running. |
The OIV.ImageViz.Engines.ImageAnalysis.Statistics.SoAutoThresholdingQuantification engine extracts a value to automaticaly threshold on a gray level image.
Three methods of classification are available: Entropy, Factorisation or Moments. The computed threshold is provided in the OIV.ImageViz.Engines.ImageAnalysis.Statistics.SoAutoThresholdingQuantification.SbAutoThresholdingDetail object.
Entropy The entropy principle defines 2 classes in the image histogram by minimizing the total classes' entropy, for more theory the reader can refers to references [1] and [2]. Considering the first-order probability histogram of an image and assuming that all symbols in the flowing equation are statistically independent, its entropy (in the Shannon sense) is defined as:
Where is the number of grayscales, the probability of occurrence of level and is the log in base 2.
Let us denote the value of the threshold and the search interval. We can define two partial entropies:
Where defines the probability of occurrence of level in the range and defines the probability of occurrence of level in the range [t+1,I2]. We search the threshold value which minimizes the sum :
Factorization The factorization method is based on the Otsu criterion (see [3] for details), i.e. minimizing the within-class variance:
Where and are respectively the probabilities occurrence and , the variances of classes and .
A faster and equivalent approach is to maximize the between-class variance:
The within-class variance calculation is based on the second-order statistics (variances) while the between-class variance calculation is based on the first order statistics (means). It is therefore simplest and faster to use this last optimization criterion. We then search the value which maximizes the between-class variance such as:
Moments The moment OIV.ImageViz.Engines.ImageSegmentation.Binarization.SoAutoThresholdingProcessing uses the moment-preserving bi-level thresholding described by W.H.Tsai in [4]. Moments of an image can be computed from its histogram in the following way:
Where is the probability of occurrence of grayscale . For the following we note the original grayscale image and the threshold image. Image can be considered as a blurred version of an ideal bi-level image which consists of pixels with only two gray values: and . The moment-preserving thresholding principle is to select a threshold value such that if all below-threshold gray values of the original image are replaced by and all above threshold gray values replaced by , then the first three moments of the original image are preserved in the resulting bi-level image. Image so obtained may be regarded as an ideal unblurred version of . Let and denote the fractions of the below-threshold pixels and the above-threshold pixels in , respectively, then the first three moments of are:
And preserving the first three moments in , means the equalities:
To find the desired threshold value , we can first solve the four equations system to obtain and , and then choose as the -tile of the histogram of . Note that and will also be obtained simultaneously as part of the solutions of system.
[1] T.Pun, Entropic thresholding: A new approach, comput. Graphics Image Process. 16, 1981, 210-239 [2] J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram" Computer Vision, Graphics and Image Processing 29, pp. 273-285, Mar. 1985 [3] Otsu, N. 1979. A thresholding selection method from grayscale histogram. IEEE Transactions on Systems, Man, and Cybernetics9(1): 62-66 [4] Tsai, W. H. 1985. Moment-preserving thresholding: A New Approach. Computer Vision, Graphics, and Image Processing 29: 377-393
AutoThresholdingQuantification {
computeMode | MODE_AUTO |
inGrayImage | NULL |
rangeMode | MIN_MAX |
intensityRangeInput | 0.0f 255.0f |
thresholdCriterion | ENTROPY |
Library references: auto_threshold_value