SoAutoThresholdingProcessing engine More...

#include <ImageViz/Engines/ImageSegmentation/Binarization/SoAutoThresholdingProcessing.h>

Inheritance diagram for SoAutoThresholdingProcessing:

Classes
class	SbAutoThresholdingDetail
	Results details of threshold by automatic segmentation. More...

Public Types
enum	ObjectLightness { BRIGHT_OBJECTS = 0 , DARK_OBJECTS = 1 }

enum	ThresholdCriterion { ENTROPY = 0 , FACTORISATION = 1 , MOMENTS = 2 }

enum	RangeMode { MIN_MAX = 0 , OTHER = 1 }

Public Types inherited from SoImageVizEngine
enum	ComputeMode { MODE_2D = 0 , MODE_3D = 1 , MODE_AUTO = 2 }
	Compute Mode This enum specifies whether the main input will be interpreted as a 3D volume or a stack of 2D images for processing. More...

enum	Neighborhood3d { CONNECTIVITY_6 = 0 , CONNECTIVITY_18 = 1 , CONNECTIVITY_26 = 2 }
	Neighborhood Connectivity 3D. More...

Public Member Functions
	SoAutoThresholdingProcessing ()
	Constructor.

Public Member Functions inherited from SoImageVizEngine
virtual SoType	getTypeId () const
	Returns the type identifier for this specific instance.

virtual void	startEvaluate ()
	Evaluate engine and dependencies in another thread without blocking the current one.

virtual void	waitEvaluate ()
	Wait for the end of engine evaluation.

virtual void	abortEvaluate ()
	Abort current processing as soon as possible.

virtual bool	isEvaluating ()
	Returns true if the engine evaluation is in progress.

Public Member Functions inherited from SoEngine
virtual int	getOutputs (SoEngineOutputList &list) const
	Returns a list of outputs in this engine.

SoEngineOutput *	getOutput (const SbName &outputName) const
	Returns a reference to the engine output with the given name.

SbBool	getOutputName (const SoEngineOutput *output, SbName &outputName) const
	Returns (in outputName) the name of the engine output (output).

SoEngine *	copy () const
	Creates and returns an exact copy of the engine.

Public Member Functions inherited from SoFieldContainer
void	setToDefaults ()
	Sets all fields in this object to their default values.

SbBool	hasDefaultValues () const
	Returns TRUE if all of the object's fields have their default values.

SbBool	fieldsAreEqual (const SoFieldContainer *fc) const
	Returns TRUE if this object's fields are exactly equal to fc's fields.

void	copyFieldValues (const SoFieldContainer *fc, SbBool copyConnections=FALSE)
	Copies the contents of fc's fields into this object's fields.

SoNONUNICODE SbBool	set (const char *fieldDataString)
	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.

SbBool	set (const SbString &fieldDataString)
	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.

void	get (SbString &fieldDataString)
	Returns the values of the fields of this object in the Open Inventor ASCII file format in the given string.

virtual int	getFields (SoFieldList &list) const
	Appends references to all of this object's fields to resultList, and returns the number of fields appended.

virtual int	getAllFields (SoFieldList &list) const
	Returns a list of fields, including the eventIn's and eventOut's.

virtual SoField *	getField (const SbName &fieldName) const
	Returns a the field of this object whose name is fieldName.

virtual SoField *	getEventIn (const SbName &fieldName) const
	Returns a the eventIn with the given name.

virtual SoField *	getEventOut (const SbName &fieldName) const
	Returns the eventOut with the given name.

SbBool	getFieldName (const SoField *field, SbName &fieldName) const
	Returns the name of the given field in the fieldName argument.

SbBool	enableNotify (SbBool flag)
	Notification at this Field Container is enabled (if flag == TRUE) or disabled (if flag == FALSE).

SbBool	isNotifyEnabled () const
	Notification is the process of telling interested objects that this object has changed.

virtual void	setUserData (void *data)
	Sets application data.

void *	getUserData (void) const
	Gets user application data.

Public Member Functions inherited from SoBase
virtual void	touch ()
	Marks an instance as modified, simulating a change to it.

virtual SbName	getName () const
	Returns the name of an instance.

virtual void	setName (const SbName &name)
	Sets the name of an instance.

void	setSynchronizable (const bool b)
	Sets this to be a ScaleViz synchronizable object.

bool	isSynchronizable () const
	Gets the ScaleViz synchronizable state of this object.

Public Member Functions inherited from SoRefCounter
void	ref () const
	Adds a reference to an instance.

void	unref () const
	Removes a reference from an instance.

void	unrefNoDelete () const
	unrefNoDelete() should be called when it is desired to decrement the reference count, but not delete the instance if this brings the reference count to zero.

int	getRefCount () const
	Returns current reference count.

void	lock () const
	lock this instance.

void	unlock () const
	unlock this instance.

Public Member Functions inherited from SoTypedObject
SbBool	isOfType (const SoType &type) const
	Returns TRUE if this object is of the type specified in type or is derived from that type.

template<typename TypedObjectClass >
SbBool	isOfType () const
	Returns TRUE if this object is of the type of class TypedObjectClass or is derived from that class.

Public Attributes
SoSFEnum	computeMode
	Select the compute Mode (2D or 3D or AUTO) Use enum ComputeMode.

SoSFEnum	objectLightness
	Select the lightness mode for object to detect.

SoSFImageDataAdapter	inGrayImage
	The input grayscale image.

SoSFVec2f	intensityRangeInput
	The input intensity range.

SoSFEnum	thresholdCriterion
	The criterion to detect thresholds on histogram.

SoSFEnum	rangeMode
	Select the input intensity range mode.

SoImageVizEngineOutput< SoSFImageDataAdapter, SoImageDataAdapter * >	outBinaryImage
	The output binary image.

SoImageVizEngineAnalysisOutput< SbAutoThresholdingDetail >	outResult
	The thresholding results.

Public Attributes inherited from SoImageVizEngine
SbEventHandler< EventArg & >	onBegin
	Event raised when the processing begins.

SbEventHandler< EventArg & >	onEnd
	Event raised when processing ends and the result is available.

SbEventHandler< EventArg & >	onProgress
	Event raised while processing is running.

Additional Inherited Members
Static Public Member Functions inherited from SoImageVizEngine
static SoType	getClassTypeId ()
	Returns the type identifier for this class.

Static Public Member Functions inherited from SoEngine
static SoType	getClassTypeId ()
	Returns the type identifier for the SoEngine class.

static SoEngine *	getByName (const SbName &name)
	Looks up engine(s) by name.

static int	getByName (const SbName &name, SoEngineList &list)
	Looks up engine(s) by name.

Static Public Member Functions inherited from SoFieldContainer
static SoType	getClassTypeId ()
	Returns the type of this class.

Static Public Member Functions inherited from SoBase
static SoType	getClassTypeId ()
	Returns type identifier for this class.

Static Public Member Functions inherited from SoTypedObject
static SoType	getClassTypeId ()
	Returns the type identifier for this class.

Detailed Description

SoAutoThresholdingProcessing engine

The SoAutoThresholdingProcessing engine computes an automatic threshold on a gray level image.

This engine computes an automatic threshold on a grayscale image i.e. separate the image in 2 classes of pixels. Three methods of classification are available: Entropy, Factorisation or Moments. The computed threshold is provided in the 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:

$H=-\sum_{i=0}^{n} p[i] \times \log(p[i])_ 2$

Where $n+1$ is the number of grayscales, $p[i]$ the probability of occurrence of level and $(x)_2$ is the log in base 2.

Let us denote $t$ the value of the threshold and $[I_1,I_2]$ the search interval. We can define two partial entropies:

$H_w[t]=-\sum_{I_1}^{t} p_1[i] \times \log(p_1[i])_2$

$H_b[t]=-\sum_{t+1}^{I_2} p_2[i] \times \log(p_2[i])_2$

Where $p_1[i]$ defines the probability of occurrence of level in the range $[I_1,t]$ and $p_2[i]$ defines the probability of occurrence of level $i$ in the range [t+1,I2]. We search the threshold value $T$ which minimizes the sum $S(t)=H_w[t]+H_b[t]$ :

$T=\arg min_t(H_w[t]+H_b[t])$

Figure 1: Example of thresholding using the entropy method

Factorization
The factorization method is based on the Otsu criterion (see [3] for details), i.e. minimizing the within-class variance:

$\sigma^2_W[t]=w_0[t] \times \sigma_0^2[t]+w_1[t] \times \sigma_1^2[t]$

Where $w_0[t]$ and $w_1[t]$ are respectively the probabilities occurrence $^2[t]$ and $^2[t]$ , the variances of classes $C_0$ and $C_1$ .

A faster and equivalent approach is to maximize the between-class variance:

$\sigma_B^2[t]=w_0[t] \times w_1[t] \times (\mu_0[t]-\mu_1[t])^2$

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 $T$ which maximizes the between-class variance such as:

$T=\arg min_t(\sigma_B^2[t])$

Figure 2: Example of thresholding using the factorization method

Moments
The moment 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:

$m_j=\sum_{i=0}^n p[z_i]^j$

Where $p[z_i]$ is the probability of occurrence of grayscale $z_i$ . For the following we note $f$ the original grayscale image and $g$ the threshold image. Image $f$ can be considered as a blurred version of an ideal bi-level image which consists of pixels with only two gray values: $z_0$ and $z_1$ . 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 $z_0$ and all above threshold gray values replaced by $z_1$ , then the first three moments of the original image are preserved in the resulting bi-level image. Image $g$ so obtained may be regarded as an ideal unblurred version of $f$ . Let $p_0$ and $p_1$ denote the fractions of the below-threshold pixels and the above-threshold pixels in $f$ , respectively, then the first three moments of $g$ are:

$m'_j=\sum_{i=0}^n p[z_i]^j\mbox{, j=0,1,2,3}$

And preserving the first three moments in $g$ , means the equalities:

$m'_j=m_j\mbox{, j=0,1,2,3}$

To find the desired threshold value $T$ , we can first solve the four equations system to obtain $p_0$ and $p_1$ , and then choose $T$ as the $p_0$ -tile of the histogram of $f$ . Note that $z_0$ and $z_1$ will also be obtained simultaneously as part of the solutions of system.

Figure 3: Example of thresholding using the moment-preserving method

[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

FILE FORMAT/DEFAULT

computeMode	MODE_AUTO
objectLightness	BRIGHT_OBJECTS
inGrayImage	NULL
intensityRangeInput	0.0f 255.0f
thresholdCriterion	ENTROPY
rangeMode	MIN_MAX

Library references: auto_threshold auto_threshold_inv

Definition at line 178 of file SoAutoThresholdingProcessing.h.

Member Enumeration Documentation

◆ ObjectLightness

enum SoAutoThresholdingProcessing::ObjectLightness

Enumerator
BRIGHT_OBJECTS	The algorithm thresholds bright objects (graylevels between T and Max(I))
DARK_OBJECTS	The algorithm thresholds dark objects (between Min(I) and T )

Definition at line 215 of file SoAutoThresholdingProcessing.h.

◆ RangeMode

enum SoAutoThresholdingProcessing::RangeMode

Enumerator
MIN_MAX	With this option the histogram is computed between the minimum and the maximum of the image.
OTHER	With this option the histogram is computed between user-defined bounds [a,b].

Definition at line 263 of file SoAutoThresholdingProcessing.h.

◆ ThresholdCriterion

enum SoAutoThresholdingProcessing::ThresholdCriterion

Enumerator
ENTROPY	The measure of dispersion used in the algorithm is the entropy of the intensity distribution.
FACTORISATION	The measure of dispersion used in the algorithm is the variance of the intensity distribution.
MOMENTS	The measure of dispersion used in the algorithm is the moments of the intensity distribution.

Definition at line 240 of file SoAutoThresholdingProcessing.h.