represents a special B-Spline and holds functions to handle fitting More...

#include <FeatureDetector/ContourDetectorBSpline.hh>

Inheritance diagram for BIAS::ContourDetectorBSpline< StorageType >:

Collaboration diagram for BIAS::ContourDetectorBSpline< StorageType >:

Public Member Functions
void	CenterOverControlPoints ()
	centers the curve by computing center over control points (not accurate!) More...

void	ComputeRegularisationMatrix ()
	computes the regularisation matrix needed for fitting algorithm. More...

	ContourDetectorBSpline ()
	standard constructor More...

	ContourDetectorBSpline (const ContourDetectorBSpline< StorageType > &toCopy)
	copy constructor More...

int	Detect (Image< StorageType > &image, std::vector< BIAS::BIASContour > &contour)
	detect function, unfinished More...

void	Displace (const double &x, const double &y)
	displace all control points More...

void	DrawControlPoints (BIAS::Image< StorageType > &img, const StorageType color[3])
	draws the control points of the B-Spline curve into image More...

void	DrawCurve (BIAS::Image< StorageType > &img, const StorageType color[3])
	draws B-Spline curve into image More...

void	DrawCurve (BIAS::Image< float > &img)
	draws B-Spline curve into image More...

void	DrawNormals (const unsigned int normalWidth, BIAS::Image< StorageType > &img, const StorageType color[3])
	draws B-Spline normals into image More...

void	Fit (BIAS::Image< StorageType > &greyImg, const double &alpha, const unsigned int normalWidth)
	fits B-Spline curve to gradient features found in the image More...

void	Fit (BIAS::Image< StorageType > &greyImg, const double &alpha, const unsigned int normalWidth, const double &minClip)
	fits B-Spline curve to gradient features found in the image More...

void	Fit (const BIAS::Image< float > &featImg, const double &alpha, const unsigned int normalWidth)
	fits B-Spline curve to gradient features found in the image More...

void	Fit (const BIAS::Image< float > &featImg, const double &alpha, const unsigned int normalWidth, const double &minClip)
	fits B-Spline curve to gradient features found in the image More...

void	GetBoundingBox (int &minX, int &minY, int &maxX, int &maxY)
	returns the parameter of the bounding box of the curve More...

ContourBSplineType::Type	GetBType ()
	returns type of B-Spline curve - possible values are Open, Closed or Cluster. More...

void	GetControlPoints (BIAS::Vector< double > &Q)
	returns a vector of control points; first entries are x-coordinates; last entries are y-coordinates More...

void	GetControlPoints (std::vector< BIAS::Vector2< double > > &cPnts)
	returns a vector of control points in x,y-coordinates More...

void	GetFeature (BIAS::Image< StorageType > &greyImg, const double &t, const unsigned int normalWidth, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point located at "t" More...

void	GetFeature (BIAS::Image< StorageType > &greyImg, const double &t, const unsigned int normalWidth, const double &minClip, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point located at "t" More...

void	GetFeature (const BIAS::Image< float > &featImg, const double &t, const unsigned int normalWidth, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point located at "t" More...

void	GetFeature (const BIAS::Image< float > &featImg, const double &t, const unsigned int normalWidth, const double &minClip, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point located at "t" More...

void	GetFeature (BIAS::Image< StorageType > &greyImg, const unsigned int normalWidth, const BIAS::Vector2< double > &curvePnt, const BIAS::Vector2< double > &normal, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point "curvePnt" More...

void	GetFeature (BIAS::Image< StorageType > &greyImg, const unsigned int normalWidth, const BIAS::Vector2< double > &curvePnt, const BIAS::Vector2< double > &normal, const double &minClip, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point "curvePnt" More...

void	GetFeature (const BIAS::Image< float > &featImg, const unsigned int normalWidth, const BIAS::Vector2< double > &curvePnt, const BIAS::Vector2< double > &normal, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point "curvePnt" More...

void	GetFeature (const BIAS::Image< float > &featImg, const unsigned int normalWidth, const BIAS::Vector2< double > &curvePnt, const BIAS::Vector2< double > &normal, const double &minClip, BIAS::Vector2< double > &res)
	returns the feature point with highest gradient along the normal in curve point "curvePnt" More...

std::vector< unsigned int >	GetMultiplePoints ()
	returns vector of multiple points; indicates which control points are modelled as edges More...

void	GetNormal (const double &t, BIAS::Vector2< double > &res)
	returns normal in curve point More...

unsigned int	GetOrder ()
	returns order of B-Spline curve More...

void	GetPoint (const double &t, BIAS::Vector2< double > &res)
	returns point on the curve More...

void	Init (const unsigned int order, const ContourBSplineType::Type bType, const std::vector< BIAS::Vector2< double > > &cPnts)
	initialisation function for simple curve without edges More...

void	Init (const unsigned int order, const ContourBSplineType::Type bType, const std::vector< unsigned int > &mPnts, const std::vector< BIAS::Vector2< double > > &cPnts)
	initialisation function for curve with edges More...

void	Init (const unsigned int order, const ContourBSplineType::Type bType, const BIAS::Vector< double > &Q)
	initialisation function for simple curve without edges More...

void	Init (const unsigned int order, const ContourBSplineType::Type bType, const std::vector< unsigned int > &mPnts, const BIAS::Vector< double > &Q)
	initialisation function for curve with edges More...

bool	Load (const std::string &filename)
	load/initialise a ContourBSpline object from an given binary file More...

ContourDetectorBSpline &	operator= (const ContourDetectorBSpline< StorageType > &toCopy)
	copy operator More...

void	Rotate (const double &angle)
	rotates curve by angle (rad) More...

bool	Save (const std::string &filename)
	saves all values which are needed to reinitialise the object to a binary file More...

void	Scale (const double &scale)
	scales curve isotropic More...

void	ScaleX (const double &scale)
	scales curve in x-direction (horizontally) More...

void	ScaleY (const double &scale)
	scales curve in y-direction (vertically) More...

void	SetControlPoints (const std::vector< BIAS::Vector2< double > > &controlPoints)
	sets new control points More...

void	SetControlPoints (const BIAS::Vector< double > &Q)
	sets new control points More...

void	SetDrawSamples (unsigned int samples)
	sets the amount of sample points used to draw the B-Spline curve. More...

void	SetDrawSampleWidth (double width)
	sets the amount of sample points used to draw the B-Spline curve. More...

void	SetFitSamples (unsigned int samples)
	sets the amount of samples used to search features along the curve in fitting algorithm. More...

void	SetFitSampleWidth (double width)
	sets the amount of samples used to search features along the curve in fitting algorithm. More...

void	SetShapeSpaceEuclidian ()
	sets spape-space for regularised fitting to euclidian similarities More...

void	SetShapeSpaceIdentity ()
	sets spape-space for regularised fitting to identity More...

void	SetShapeSpaceMatrix (BIAS::Matrix< double > &shapeSpace)
	sets spape-space for regularised fitting More...

void	SetShapeSpacePlanarAffin ()
	sets spape-space for regularised fitting to planar affine transformations More...

void	SetSubShapeSpaceEuclidian ()
	set invariant shape-space for regularised fitting to euclidian similarities More...

void	SetSubShapeSpaceIdentity ()
	set invariant shape-space for regularised fitting to identity More...

void	SetSubShapeSpaceMatrix (BIAS::Matrix< double > &subShapeSpace)
	set invariant shape-space for regularised fitting More...

void	SetSubShapeSpacePlanarAffin ()
	set invariant shape-space for regularised fitting to planar affine transformation More...

void	SetSubShapeSpaceZero ()
	set invariant shape-space for regularised fitting to zero More...

virtual	~ContourDetectorBSpline ()
	destructor More...

Static Public Member Functions
static ContourDetectorBSpline	Cluster (const std::vector< ContourDetectorBSpline< StorageType > > &bSplines)
	builds a new ContourBspline object from given vector of ContourBSpline objects More...

Protected Member Functions
void	CPntsToQ_ (const std::vector< BIAS::Vector2< double > > &cPnts)
	converts a given control point vector in x,y-coordinates to the internal used flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates More...

void	CPntsToQ_ (const std::vector< BIAS::Vector2< double > > &cPnts, BIAS::Vector< double > &Q)
	converts a given control point vector in x,y-coordinates to the internal used flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates More...

void	Fit_ (const BIAS::Image< float > &featImg, const double &alpha, const unsigned int normalWidth)
	fits the curve to a features curve with regularized fitting More...

void	Fit_ (const BIAS::Image< float > &featImg, const double &alpha, const unsigned int normalWidth, const double &minClip)
	fits the curve to a features curve with regularized fitting More...

void	GetFeatImage_ (BIAS::Image< StorageType > &greyImg, BIAS::Image< float > &featImg, const unsigned int normalWidth)
	converts given grey image to gradient image with ROI More...

void	GetNormal_ (const unsigned int numSpan, const double &s, BIAS::Vector2< double > &res)
	computes a normal at curve point which is parametrized by "numSpan" and "s" More...

void	GetPoint_ (const unsigned int numSpan, const double &s, BIAS::Vector2< double > &res)
	computes a point on curve More...

void	GetPointAndParamVec_ (const unsigned int numSpan, const double &s, BIAS::Vector< double > &resParamVec, BIAS::Vector2< double > &res)
	computes a point on curve and a parameter vector (byproduct) which can be used for computing a special matrix (U) in the fitting algorithm More...

void	InitCurve_ ()
	initializes curve for drawing or for getting points on it More...

void	QToCPnts_ (std::vector< BIAS::Vector2< double > > &cPnts)
	converts a flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates to a control point vector with x,y-coordinates More...

void	QToCPnts_ (const BIAS::Vector< double > &Q, std::vector< BIAS::Vector2< double > > &cPnts)
	converts a flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates to a control point vector with x,y-coordinates More...

void	TestInit_ (const unsigned int order, const ContourBSplineType::Type bType, const std::vector< unsigned int > &mPnts, const BIAS::Vector< double > &Q)
	tests if B-Spline curve with given parameter can get initialized. More...

Protected Attributes
bool	curveInitialised_
	bool to indicate a change of control points More...

std::vector< std::vector < BIAS::Vector< double > > >	curveVectors_
	vectors needed to draw curve (determined by InitCurve_) More...

unsigned int	drawSamples_
	amount of samples used in drawing algorithm More...

double *	drawSampleWidth_
	distance of sample points in basis used in drawing algorithm More...

unsigned int	fitSamples_
	amount of samples used in fitting algorithm More...

double *	fitSampleWidth_
	distance of sample points in basis used in fitting algorithm More...

ContourBSplineData *	pData_
	pointer to data of B-Spline curve initialised by call of Init(...) More...

ContourBSplineShapeMatrix *	pRegMatrix_
	pointer to regularisation matrix object More...

ContourBSplineShapeMatrix *	pShapeSpaceMatrix_
	pointer to shape-space matrix object More...

ContourBSplineShapeMatrix *	pSubShapeSpaceMatrix_
	pointer to invariant shape-space matrix object More...

BIAS::Vector< double >	Q_
	vector of control points; first entries are x-coordinates; last entries are y-coordinates More...

Private Member Functions
void	AddDebugLevel (const long int lv)

void	AddDebugLevel (const std::string &name)

long	ConsumeNextFreeDebuglevel_ ()
	returns the next available debuglevel More...

bool	DebugLevelIsSet (const long int lv) const

bool	DebugLevelIsSet (const std::string &name) const

int	GetDebugLevel () const

std::ostream &	GetDebugStream () const

void	GetDebugStream (std::ostream &os) const

int	GetMaxNumContours ()
	return maximum number of contours More...

long int	Name2DebugLevel (const std::string &name) const
	looks up a debuglevel in the internal map, returns 0 if not found More...

long int	NewDebugLevel (const std::string &name)
	creates a new debuglevel More...

void	PrintDebugLevel (std::ostream &os=std::cout) const

void	RemoveDebugLevel (const long int lv)

void	RemoveDebugLevel (const std::string &name)

void	SetDebugLevel (const long int lv)

void	SetDebugLevel (const std::string &name)

void	SetDebugStream (const std::ostream &os)

void	SetMaxNumContours (const int maxnum)

void	ShowDebugLevel (std::ostream &os=std::cout) const
	prints all internally known debuglevels More...

Static Private Member Functions
static long int	GetGlobalDebugLevel ()

static void	SetGlobalDebugLevel (long int lev)

Private Attributes
long int	_liDebugLevel

long int	_liNextDebugLevel
	new concept, debuglevel are managed here in the debug class More...

std::map< std::string, long int >	_String2Debuglevel

std::vector< BIAS::BIASContour >	detectedContours_

int	dMaxNumContours_
	maximum number of corners to return More...

Static Private Attributes
static std::ostream	_zDebugStream

static long int	GlobalDebugLevel = 0

Detailed Description

template<class StorageType>
class BIAS::ContourDetectorBSpline< StorageType >

represents a special B-Spline and holds functions to handle fitting

This class represents a special B-Spline curve for "Active Contours". Based on book: "Active Contours" by Andrew Blake and Michael Isard. (http://www.robots.ox.ac.uk/~contours/)

Class offers functions to manipulate Position, Orientation and Scale of Curve, Drawing and Fitting Curve to gradient features in images.

USAGE construction: ContourBSpline bspline; //call constructor bspline.Init(...); //initialise B-Spline

The call of the Init(...) function looksup if the basis of the B-Spline is already computed. If not, a new ContourBSplineData Objects becomes generated, which holds data of this Object. If a B-Spline with same basis is available the pointer to the available ContourBSplineData gets copied. The purpose is to save/share memory. This is hidden to users of this class. After the Init(..) call, you can draw and manipulate the B-Spline.

For fitting you have to set (nonrecurring) Shape-Space, invariant Shape-Space (SubShapeSpace) and compute the Regularisation-Matrix. Look at "Active Contours" chapter 6 for details. In common cases you want to set Shape-Space to identity (SetShapeSpaceIdentity()) and invariant Shape-Space to planar affin transformations (SetSubShapeSpacePlanarAffin()).

USAGE fitting: bspline.SetShapeSpace...(...); bspline.SetSubShapeSpace...(...); bspline.ComputeRegularisationMatrix(); bspline.Fit(...);

The functions Set*ShapeSpace...(...) and ComputeRegulatisationMatrix() register/generate a ContourBSplineShapeMatrix object. The purpose is to save/share memory. This should be transparent to users of this class.

Author: Marcel Lilienthal, 2005

Examples:: ExampleContourDetectorBSpline.cpp.

Definition at line 103 of file ContourDetectorBSpline.hh.

Constructor & Destructor Documentation

template<class StorageType >

ContourDetectorBSpline::ContourDetectorBSpline ( )

standard constructor

Sets pointers to NULL and initialises some default values for Drawing and Fitting.

Definition at line 42 of file ContourDetectorBSpline.cpp.

template<class StorageType>

BIAS::ContourDetectorBSpline< StorageType >::ContourDetectorBSpline ( const ContourDetectorBSpline< StorageType > & toCopy )

inline

copy constructor

Parameters

[in] toCopy object to copy

Uses operator= to copy referred B-Spline as new Object.

Definition at line 123 of file ContourDetectorBSpline.hh.

template<class StorageType >

ContourDetectorBSpline::~ContourDetectorBSpline ( )

virtual

destructor

Unregisters ContourBSplineData object (with reference counting). Unregisters ContourBSplineShapeMatrix objects (with reference counting).

Definition at line 55 of file ContourDetectorBSpline.cpp.

Member Function Documentation

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::CenterOverControlPoints ( )

inline

centers the curve by computing center over control points (not accurate!)

Definition at line 1249 of file ContourDetectorBSpline.hh.

template<class StorageType >

ContourDetectorBSpline< StorageType > ContourDetectorBSpline::Cluster ( const std::vector< ContourDetectorBSpline< StorageType > > & bSplines )

static

builds a new ContourBspline object from given vector of ContourBSpline objects

Parameters

[in] bSplines vector of ContourBSpline objects to cluster

With this function you can cluster B-Spline curves in one B-Spline curve. For example you can represent the contour of glasses with one B-Spline curve constructed of two closed B-Spline curves.

Definition at line 76 of file ContourDetectorBSpline.cpp.

References BIAS::ContourBSplineData::numBasePolynoms_, BIAS::ContourDetectorBSpline< StorageType >::pData_, BIAS::ContourDetectorBSpline< StorageType >::Q_, and BIAS::ContourBSplineData::Register().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::ComputeRegularisationMatrix ( )

inline

computes the regularisation matrix needed for fitting algorithm.

The regularisation matrix for the fitting algorithm gets computed. You have to set shape-space and invariant shape-space before! Different to the book "Active Contours" the regularisation factor alpha isnt included in the resulting matrix.

Usage: bspline.SetShapeSpace...(...); bspline.SetSubShapeSpace...(...); bspline.ComputeRegularisationMatrix();

Definition at line 1356 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::ComputeRegularisationMatrix().

template<class StorageType >

void ContourDetectorBSpline::CPntsToQ_ ( const std::vector< BIAS::Vector2< double > > & cPnts )

protected

converts a given control point vector in x,y-coordinates to the internal used flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates

Parameters

[in] cPnts vector of control points in x,y-coordinates

Definition at line 754 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::CPntsToQ_	(	const std::vector< BIAS::Vector2< double > > &	cPnts,
		BIAS::Vector< double > &	Q
	)

protected

converts a given control point vector in x,y-coordinates to the internal used flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates

Parameters

[in]	cPnts	vector of control points in x,y-coordinates
[out]	Q	vector of control points; first entries are x-coordinates; last entries are y-coordinates

Definition at line 767 of file ContourDetectorBSpline.cpp.

References TNT::Vector< T >::newsize().

template<class StorageType >

int ContourDetectorBSpline::Detect	(	Image< StorageType > &	image,
		std::vector< BIAS::BIASContour > &	contour
	)

virtual

detect function, unfinished

Implements BIAS::ContourDetectorBase< StorageType >.

Definition at line 65 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::Displace	(	const double &	x,
		const double &	y
	)

inline

displace all control points

Parameters

[in]	x	x-offset to x-coordinates of control points
[in]	y	y-offset to y-coordinates of control points

Definition at line 1208 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::DrawControlPoints	(	BIAS::Image< StorageType > &	img,
		const StorageType	color[3]
	)

draws the control points of the B-Spline curve into image

Parameters

[out]	img	output image
[in]	color

Definition at line 618 of file ContourDetectorBSpline.cpp.

References BIAS::ImageDraw< StorageType >::CircleCenterFilled().

template<class StorageType >

void ContourDetectorBSpline::DrawCurve	(	BIAS::Image< StorageType > &	img,
		const StorageType	color[3]
	)

draws B-Spline curve into image

Parameters

[out]	img	output image
[in]	color	Draws B-Spline curve into given image. Uses amount of samples given by SetDrawSampleWidth(...) of SetDrawSamples(...). If amount of samples isnt given by these function a default amount is used.

Definition at line 634 of file ContourDetectorBSpline.cpp.

References BIAS::ImageDraw< StorageType >::Line().

template<class StorageType >

void ContourDetectorBSpline::DrawCurve ( BIAS::Image< float > & img )

draws B-Spline curve into image

Parameters

[out] img output image

Draws B-Spline curve into given image. Uses amount of samples given by SetDrawSampleWidth(...) or SetDrawSamples(...). If amount of samples isnt given by these function a default amount is used.

Definition at line 669 of file ContourDetectorBSpline.cpp.

References BIAS::ImageDraw< StorageType >::Line().

template<class StorageType >

void ContourDetectorBSpline::DrawNormals	(	const unsigned int	normalWidth,
		BIAS::Image< StorageType > &	img,
		const StorageType	color[3]
	)

draws B-Spline normals into image

Parameters

[out] img output image

Draws normals along the B-Spline curve into given image. Uses amount of samples given by SetFitSampleWidth(...) or SetFitSamples(...). If amount of samples isnt given a default amount is used.

Definition at line 704 of file ContourDetectorBSpline.cpp.

References BIAS::ImageDraw< StorageType >::Line().

template<class StorageType >

void ContourDetectorBSpline::Fit	(	BIAS::Image< StorageType > &	greyImg,
		const double &	alpha,
		const unsigned int	normalWidth
	)

fits B-Spline curve to gradient features found in the image

Parameters

[in]	greyImg	input image
[in]	alpha	regularisation factor for fitting
[in]	normalWidth	width of normal for searching gradient features

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for features (normalWidth).

Along the curve normals get computed. Number of normals depends on variable fitSampleWidth_ (SetFitSampleWidth(...)). Along the normals the feature points get assigned by the image points width highest gradient and distance less then normalWidth from curve (in both directions). Thus a feature curve is given.

The fitting algorithm is described in the book "Active Contours", chapter 6. The parameter alpha determines the regularisation of the fitting. For numeric stability alpha has to be greater than 0 even if no regularisation is forced. the regularisation determines how the curve should be fitted to the feature curve. high values of alpha cause the fitted curve to keep the ground shape and only adapt position, orientation and scale. small values causes a possibly distorted shape which could have nothing in common with the initial curve.

Definition at line 210 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Fit	(	BIAS::Image< StorageType > &	greyImg,
		const double &	alpha,
		const unsigned int	normalWidth,
		const double &	minClip
	)

fits B-Spline curve to gradient features found in the image

Parameters

[in]	greyImg	input image
[in]	alpha	regularisation factor for fitting
[in]	normalWidth	width of normal for searching gradient features
[in]	minClip	value of image gradients which are discarded in the search after feature points

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for features (normalWidth).

Along the curve normals get computed. Number of normals depends on variable fitSampleWidth_ (SetFitSampleWidth(...)). Along the normals the feature points get assigned by the image points width highest gradient and distance less then normalWidth from curve (in both directions). Image gradients with a value less than parameter minClip are discarded. Thus a feature curve is given.

The fitting algorithm is described in the book "Active Contours", chapter 6. The parameter alpha determines the regularisation of the fitting. For numeric stability alpha has to be greater than 0 even if no regularisation is forced. the regularisation determines how the curve should be fitted to the feature curve. high values of alpha cause the fitted curve to keep the ground shape and only adapt position, orientation and scale. small values causes a possibly distorted shape which could have nothing in common with the initial curve.

Definition at line 225 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Fit	(	const BIAS::Image< float > &	featImg,
		const double &	alpha,
		const unsigned int	normalWidth
	)

fits B-Spline curve to gradient features found in the image

Parameters

[in]	featImg	input gradient image
[in]	alpha	regularisation factor for fitting
[in]	normalWidth	width of normal for searching gradient features

Along the curve normals get computed. Number of normals depends on variable fitSampleWidth_ (SetFitSampleWidth(...)). Along the normals the feature points get assigned by the image points width highest gradient and distance less then normalWidth from curve (in both directions). Thus a feature curve is given.

The fitting algorithm is described in the book "Active Contours", chapter 6. The parameter alpha determines the regularisation of the fitting. For numeric stability alpha has to be greater than 0 even if no regularisation is forced. the regularisation determines how the curve should be fitted to the feature curve. high values of alpha cause the fitted curve to keep the ground shape and only adapt position, orientation and scale. small values causes a possibly distorted shape which could have nothing in common with the initial curve.

Definition at line 241 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Fit	(	const BIAS::Image< float > &	featImg,
		const double &	alpha,
		const unsigned int	normalWidth,
		const double &	minClip
	)

fits B-Spline curve to gradient features found in the image

Parameters

[in]	featImg	input gradient image
[in]	alpha	regularisation factor for fitting
[in]	normalWidth	width of normal for searching gradient features
[in]	minClip	value of image gradients which are discarded in the search after feature points

Along the curve normals get computed. Number of normals depends on variable fitSampleWidth_ (SetFitSampleWidth(...)). Along the normals the feature points get assigned by the image points width highest gradient and distance less then normalWidth from curve (in both directions). Image gradients with a value less than parameter minClip are discarded. Thus a feature curve is given.

The fitting algorithm is described in the book "Active Contours", chapter 6. The parameter alpha determines the regularisation of the fitting. For numeric stability alpha has to be greater than 0 even if no regularisation is forced. the regularisation determines how the curve should be fitted to the feature curve. high values of alpha cause the fitted curve to keep the ground shape and only adapt position, orientation and scale. small values causes a possibly distorted shape which could have nothing in common with the initial curve.

Definition at line 251 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Fit_	(	const BIAS::Image< float > &	featImg,
		const double &	alpha,
		const unsigned int	normalWidth
	)

protected

fits the curve to a features curve with regularized fitting

Parameters

[in]	featImage	input gradient image
[in]	alpha	regularisation factor
[in]	normalWidth	length of normal used to search for feature curve

Internal used function to fit the B-Spline curve to a feature curve. The feature curve is determined by searching for image points with highest gradients along the normals at the curve. Parameter "normalWidth" determines the maximal distance of feature point to curve (in both directions).

More details in function Fit(..) and book "Active Contours" chapter 6.

Definition at line 977 of file ContourDetectorBSpline.cpp.

References BIAS::SVD::Invert(), BIAS::Matrix< T >::Mult(), BIAS::Matrix< T >::Multiply(), BIAS::Matrix< T >::MultLeft(), BIAS::Vector< T >::OuterProduct(), BIAS::Vector< T >::SetZero(), and BIAS::Matrix< T >::SetZero().

template<class StorageType >

void ContourDetectorBSpline::Fit_	(	const BIAS::Image< float > &	featImg,
		const double &	alpha,
		const unsigned int	normalWidth,
		const double &	minClip
	)

protected

fits the curve to a features curve with regularized fitting

Parameters

[in]	featImage	input gradient image
[in]	alpha	regularisation factor
[in]	normalWidth	length of normal used to search for feature curve
[in]	minClip	gradients less than "minClip" are discarded in search for feature points

Internal used function to fit the B-Spline curve to a feature curve. The feature curve is determined by searching for image points with highest gradients along the normals at the curve. Parameter "normalWidth" determines the maximal distance of feature point to curve (in both directions).

More details in function Fit(..) and book "Active Contours" chapter 6.

Definition at line 1074 of file ContourDetectorBSpline.cpp.

References BIAS::SVD::Invert(), BIAS::Matrix< T >::Mult(), BIAS::Matrix< T >::Multiply(), BIAS::Matrix< T >::MultLeft(), BIAS::Vector< T >::OuterProduct(), BIAS::Vector< T >::SetZero(), and BIAS::Matrix< T >::SetZero().

template<class StorageType >

void ContourDetectorBSpline::GetBoundingBox	(	int &	minX,
		int &	minY,
		int &	maxX,
		int &	maxY
	)

returns the parameter of the bounding box of the curve

Parameters

[out]	minX	x-coordinate of top left corner of bounding box
[out]	minY	y-coordinate of top left corner of bounding box
[out]	maxX	x-coordinate of bottom right corner of bounding box
[out]	maxX	y-coordinate of bottom right corner of bounding box

Definition at line 583 of file ContourDetectorBSpline.cpp.

template<class StorageType >

ContourBSplineType::Type BIAS::ContourDetectorBSpline< StorageType >::GetBType ( )

inline

returns type of B-Spline curve - possible values are Open, Closed or Cluster.

Definition at line 1418 of file ContourDetectorBSpline.hh.

template<class StorageType>

void BIAS::ContourDetectorBSpline< StorageType >::GetControlPoints ( BIAS::Vector< double > & Q )

inline

returns a vector of control points; first entries are x-coordinates; last entries are y-coordinates

Parameters

[out] Q vector of control points

Definition at line 630 of file ContourDetectorBSpline.hh.

template<class StorageType>

void BIAS::ContourDetectorBSpline< StorageType >::GetControlPoints ( std::vector< BIAS::Vector2< double > > & cPnts )

inline

returns a vector of control points in x,y-coordinates

Parameters

[out] cPnts vector of control points in x,y-coordinates

Definition at line 636 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::GetFeatImage_	(	BIAS::Image< StorageType > &	greyImg,
		BIAS::Image< float > &	featImg,
		const unsigned int	normalWidth
	)

protected

converts given grey image to gradient image with ROI

Parameters

[in]	greyImg	input grey image
[out]	featImg	output gradient image
[in]	normalWidth	used to determine ROI

Converts a given grey image to a gradient image. gradient image is needed for the fitting algorithm. In the fitting algorithm only a small region of the image is of interest for searching feature points. Thus a ROI gets computed from a bounding box of the curve and the parameter "normalWidth". Parameter "normalWidth" determines how far along curve normal a feature point gets searched.

Definition at line 1174 of file ContourDetectorBSpline.cpp.

References BIAS::Image< StorageType >::FillImageWithConstValue(), BIAS::GradientSobel3x3< InputStorageType, OutputStorageType >::Filter(), BIAS::ImageBase::GetChannelCount(), BIAS::ImageBase::GetHeight(), BIAS::ImageBase::GetWidth(), BIAS::Image< StorageType >::Init(), BIAS::ImageBase::SetROICorners(), and BIAS::ImageBase::UnsetROI().

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	BIAS::Image< StorageType > &	greyImg,
		const double &	t,
		const unsigned int	normalWidth,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point located at "t"

Parameters

[in]	greyImg	input grey image
[in]	t	sample point in [0,1]
[in]	normalWidth	length of normal for searching gradient feature along normal in curve point at "t"
[out]	res	resulting feature point

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for feature (normalWidth).

Parameter "t" is used to sample the basis of the curve and computing the curve point at "t". the normal at this curve point gets computed. Along this normal (in both directions) the image point width the highest gradient gets determined. The resulting features point has distance less equal than "normalWidth" to curve point.

Definition at line 476 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	BIAS::Image< StorageType > &	greyImg,
		const double &	t,
		const unsigned int	normalWidth,
		const double &	minClip,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point located at "t"

Parameters

[in]	greyImg	input grey image
[in]	t	sample point in [0,1]
[in]	normalWidth	length of normal for searching gradient feature along normal in curve point at "t"
[in]	minClip	value of image gradients which are discarded in the search after the feature point
[out]	res	resulting feature point

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for feature (normalWidth).

Parameter "t" is used to sample the basis of the curve and computing the curve point at "t". the normal at this curve point gets computed. Along this normal (in both directions) the image point width the highest gradient gets determined. image gradients less than "minClip" get discarded. The resulting features point has distance less equal then "normalWidth" to curve point.

Definition at line 491 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	const BIAS::Image< float > &	featImg,
		const double &	t,
		const unsigned int	normalWidth,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point located at "t"

Parameters

[in]	featImg	input gradient image
[in]	t	sample point in [0,1]
[in]	normalWidth	length of normal for searching gradient feature along normal in curve point at "t"
[out]	res	resulting feature point

Parameter "t" is used to sample the basis of the curve and computing the curve point at "t". the normal at this curve point gets computed. Along this normal (in both directions) the image point width the highest gradient gets determined. The resulting feature point has distance less equal than "normalWidth" to curve point.

Definition at line 507 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	const BIAS::Image< float > &	featImg,
		const double &	t,
		const unsigned int	normalWidth,
		const double &	minClip,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point located at "t"

Parameters

[in]	featImg	input gradient image
[in]	t	sample point in [0,1]
[in]	normalWidth	length of normal for searching gradient feature along normal at curve point at "t"
[in]	minClip	value of image gradients which are discarded in the search after the feature point
[out]	res	resulting feature point

Parameter "t" is used to sample the basis of the curve and computing the curve point at "t". the normal at this curve point gets computed. Along this normal (in both directions) the image point width the highest gradient gets determined. image gradients less than "minClip" get discarded. The resulting feature point has distance less equal than "normalWidth" to curve point.

Definition at line 545 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::GetFeature	(	BIAS::Image< StorageType > &	greyImg,
		const unsigned int	normalWidth,
		const BIAS::Vector2< double > &	curvePnt,
		const BIAS::Vector2< double > &	normal,
		BIAS::Vector2< double > &	res
	)

inline

returns the feature point with highest gradient along the normal in curve point "curvePnt"

Parameters

[in]	greyImg	input grey image
[in]	normalWidth	length of normal for searching gradient feature along normal at curve point "curvePnt"
[in]	curvePnt	point on the curve which is start point for the search for feature point
[in]	normal	normal at curve point "curvePnt"
[out]	res	resulting feature point

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for feature (normalWidth).

Along the normal (in both directions) at curve point "curvePnt" the image point width the highest gradient gets determined. The resulting feature point has distance less equal than "normalWidth" to the curve point.

Definition at line 1431 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::GetFeature	(	BIAS::Image< StorageType > &	greyImg,
		const unsigned int	normalWidth,
		const BIAS::Vector2< double > &	curvePnt,
		const BIAS::Vector2< double > &	normal,
		const double &	minClip,
		BIAS::Vector2< double > &	res
	)

inline

returns the feature point with highest gradient along the normal in curve point "curvePnt"

Parameters

[in]	greyImg	input grey image
[in]	normalWidth	length of normal for searching gradient feature along normal at curve point "curvePnt"
[in]	curvePnt	point on the curve which is start point for the search for feature point
[in]	normal	normal at curve point "curvePnt"
[in]	minClip	value of image gradients which are discarded in the search for the feature point
[out]	res	resulting feature point

The input image gets converted to a single gradient image. A ROI gets computed from controlpoints and the width to search for feature (normalWidth).

Along the normal (in both directions) at curve point "curvePnt" the image point width the highest gradient gets determined. Image gradients less than "minClip"get discarded.The resulting feature point has distance less equal than "normalWidth" to the curve point.

Definition at line 1443 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	const BIAS::Image< float > &	featImg,
		const unsigned int	normalWidth,
		const BIAS::Vector2< double > &	curvePnt,
		const BIAS::Vector2< double > &	normal,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point "curvePnt"

Parameters

[in]	featImg	input gradient image
[in]	normalWidth	length of normal for searching gradient feature along normal at curve point "curvePnt"
[in]	curvePnt	point on the curve which is start point for the search for feature point
[in]	normal	normal at curve point "curvePnt"
[out]	res	resulting feature point

Along the normal (in both directions) at curve point "curvePnt" the image point width the highest gradient gets determined. The resulting feature point has distance less equal than "normalWidth" to the curve point.

Definition at line 869 of file ContourDetectorBSpline.cpp.

References BIAS::ImageBase::GetHeight(), BIAS::ImageBase::GetWidth(), and BIAS::Image< StorageType >::PixelValue().

template<class StorageType >

void ContourDetectorBSpline::GetFeature	(	const BIAS::Image< float > &	featImg,
		const unsigned int	normalWidth,
		const BIAS::Vector2< double > &	curvePnt,
		const BIAS::Vector2< double > &	normal,
		const double &	minClip,
		BIAS::Vector2< double > &	res
	)

returns the feature point with highest gradient along the normal in curve point "curvePnt"

Parameters

[in]	greyImg	input gradient image
[in]	normalWidth	length of normal for searching gradient feature along normal at curve point "curvePnt"
[in]	curvePnt	point on the curve which is start point for the search for feature point
[in]	normal	normal at curve point "curvePnt"
[in]	minClip	value of image gradients which are discarded in the search for the feature point
[out]	res	resulting feature point

Along the normal (in both directions) at curve point "curvePnt" the image point width the highest gradient gets determined. Image gradients less than "minClip"get discarded.The resulting feature point has distance less equal than "normalWidth" to the curve point.

Definition at line 922 of file ContourDetectorBSpline.cpp.

References BIAS::ImageBase::GetHeight(), BIAS::ImageBase::GetWidth(), and BIAS::Image< StorageType >::PixelValue().

template<class StorageType >

std::vector< unsigned int > BIAS::ContourDetectorBSpline< StorageType >::GetMultiplePoints ( )

inline

returns vector of multiple points; indicates which control points are modelled as edges

Definition at line 1424 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::GetNormal	(	const double &	t,
		BIAS::Vector2< double > &	res
	)

returns normal in curve point

Parameters

[in]	t	sample point in [0,1]
[out]	res	resulting normal

Parameter "t" is used to sample the basis of curve and compute the normal in curve point at "t".

Definition at line 448 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::GetNormal_	(	const unsigned int	numSpan,
		const double &	s,
		BIAS::Vector2< double > &	res
	)

protected

computes a normal at curve point which is parametrized by "numSpan" and "s"

Parameters

[in]	numSpan	span of curve in which normal is located
[in]	s	sample point of span in [0,1]
[out]	res	resulting normal

This function is internal used to compute a normal at a curve point. Each B-Spline-Curve consists of a number of spans. The curve point is parameterized by "numSpan" and "s".

Definition at line 835 of file ContourDetectorBSpline.cpp.

References BIAS::Vector2< T >::NormL2(), and BIAS::Vector< T >::ScalarProduct().

template<class StorageType >

unsigned int BIAS::ContourDetectorBSpline< StorageType >::GetOrder ( )

inline

returns order of B-Spline curve

Definition at line 1412 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::GetPoint	(	const double &	t,
		BIAS::Vector2< double > &	res
	)

returns point on the curve

Parameters

[in]	t	sample point in [0,1]
[out]	res	resulting point

Parameter "t" is used to sample the basis of curve and compute the curve point at "t".

Definition at line 420 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::GetPoint_	(	const unsigned int	numSpan,
		const double &	s,
		BIAS::Vector2< double > &	res
	)

inlineprotected

computes a point on curve

Parameters

[in]	numSpan	span of curve in which point is located
[in]	s	sample point of span in [0,1]
[out]	res	resulting point on curve

This function is internal used to compute a point on a curve. Each B-Spline-Curve consist of a number of spans. Parameter "numSpan" and "s" are used to sample the curve basis.

Definition at line 1456 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::GetPointAndParamVec_	(	const unsigned int	numSpan,
		const double &	s,
		BIAS::Vector< double > &	resParamVec,
		BIAS::Vector2< double > &	res
	)

protected

computes a point on curve and a parameter vector (byproduct) which can be used for computing a special matrix (U) in the fitting algorithm

Parameters

[in]	numSpan	span of curve in which point is located
[in]	s	sample point of span in [0,1]
[out]	resParamVec	special parameter vector (byproduct) used in fitting algorithm
[out]	res	resulting point on curve

This function is internal used to compute a point on a curve. Each B-Spline-Curve consist of a number of spans. Parameter "numSpan" and "s" are used to sample the curve basis. Parameter "resParamVec" is a byproduct of curve point computation. It is build up on basis of "s" and can be used to compute the special matrix "U" in fitting algorihm. More information about "U" can be found in the book "Active Contours".

Definition at line 806 of file ContourDetectorBSpline.cpp.

References TNT::Vector< T >::newsize(), and BIAS::Vector< T >::ScalarProduct().

template<class StorageType >

void ContourDetectorBSpline::Init	(	const unsigned int	order,
		const ContourBSplineType::Type	bType,
		const std::vector< BIAS::Vector2< double > > &	cPnts
	)

initialisation function for simple curve without edges

Parameters

[in]	order	order of B-Spline curve (implies degree)
[in]	bType	type of curve: Open or Closed
[in]	cPnts	control points of curve with x,y-coordinates

Generates/registers basis for B-Spline curve without edges transparently.

Example: bspline.Init(order,BIAS::ContourBSplineType::Open,cPnts);

Definition at line 123 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Init	(	const unsigned int	order,
		const ContourBSplineType::Type	bType,
		const std::vector< unsigned int > &	mPnts,
		const std::vector< BIAS::Vector2< double > > &	cPnts
	)

initialisation function for curve with edges

Parameters

[in]	order	order of B-Spline curve (implies degree)
[in]	bType	type of curve: Open or Closed
[in]	mPnts	multiplicity of control points
[in]	cPnts	control points of curve with x,y-coordinates

Generates/registers basis for B-Spline curve with edges transparently.

Param vector mPnts must have same length as vector of control points. A multiplicity of "order-1" at index "i" implies an edge in the curve at control point with index "i".

If type of the curve is "Open" the first control point with an edge is only possible at index "order-1" (begin counting with zero). Last possible control point with an edge is "last index - (order-1)". Edges in control points must have distance in indices of "order-1". Example: If order==3 (quadratic B-Spline) and there is an edge in control point with index 2 (mPnts[2]=2) then the next possible edge is in control point with index 4 (mPnts[4]=2).

If type of curve is "Closed" the indicies of control points with edges must have periodic distance of "order-1". Exampe: If order==3 (quadratic B-Spline) and there is an edge in control point with index 0 (mPnts[0]=2) then the next possible edge is in control point with index 2 (mPnts[2]=2). Periodic distance means that the last possible edge is at index "last index - 1" in this case.

Definition at line 109 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::Init	(	const unsigned int	order,
		const ContourBSplineType::Type	bType,
		const BIAS::Vector< double > &	Q
	)

initialisation function for simple curve without edges

Parameters

[in]	order	order of B-Spline curve (implies degree)
[in]	bType	type of curve: Open or Closed
[in]	Q	control points of curve first all x-coordinates then all y-coordinates (adopted from book "Active Contours") Q= (x) - vector of x-coordinates (y) - vector of y-coordinates

Generates/registers basis for B-Spline curve without edges transparently.

Definition at line 159 of file ContourDetectorBSpline.cpp.

References BIAS::Vector< T >::Size().

template<class StorageType >

void ContourDetectorBSpline::Init	(	const unsigned int	order,
		const ContourBSplineType::Type	bType,
		const std::vector< unsigned int > &	mPnts,
		const BIAS::Vector< double > &	Q
	)

initialisation function for curve with edges

Parameters

[in]	order	order of B-Spline curve (implies degree)
[in]	bType	type of curve: Open or Closed
[in]	mPnts	multiplicity of control points
[in]	Q	control points of curve first all x-coordinates then all y-coordinates (adopted from book "Active Contours") Q= (x) - vector of x-coordinates (y) - vector of y-coordinates

Generates/registers basis for B-Spline curve with edges transparently.

Param vector mPnts must have same length as vector of control points. A multiplicity of "order-1" at index "i" implies an edge in the curve at control point with index "i".

If type of the curve is "Open" the first control point with an edge is only possible at index "order-1" (begin counting with zero). Last possible control point with an edge is "last index - (order-1)". Edges in control points must have distance in indices of "order-1". Example: If order==3 (quadratic B-Spline) and there is an edge in control point with index 2 (mPnts[2]=2) then the next possible edge is in control point with index 4 (mPnts[4]=2).

If type of curve is "Closed" the indicies of control points with edges must have periodic distance of "order-1". Exampe: If order==3 (quadratic B-Spline) and there is an edge in control point with index 0 (mPnts[0]=2) then the next possible edge is in control point with index 2 (mPnts[2]=2). Periodic distance means that the last possible edge is at index "last index - 1" in this case.

Definition at line 139 of file ContourDetectorBSpline.cpp.

References BIAS::ContourBSplineData::Register().

template<class StorageType >

void ContourDetectorBSpline::InitCurve_ ( )

protected

initializes curve for drawing or for getting points on it

Computes curve vectors needed for determine the shape of the curve. Depends on the basis and the current controlpoints.

Definition at line 170 of file ContourDetectorBSpline.cpp.

References BIAS::Matrix< T >::clear(), BIAS::Matrix< T >::GetCol(), and BIAS::Matrix< T >::MultLeft().

template<class StorageType >

bool ContourDetectorBSpline::Load ( const std::string & filename )

load/initialise a ContourBSpline object from an given binary file

Parameters

[in] filename name of source file

Definition at line 319 of file ContourDetectorBSpline.cpp.

References BIAS::ContourBSplineType::Cluster, and BIAS::ContourBSplineData::Register().

template<class StorageType >

ContourDetectorBSpline< StorageType > & BIAS::ContourDetectorBSpline< StorageType >::operator= ( const ContourDetectorBSpline< StorageType > & toCopy )

inline

copy operator

Parameters

[in] toCopy object to copy

Definition at line 1161 of file ContourDetectorBSpline.hh.

template<class StorageType >

void ContourDetectorBSpline::QToCPnts_ ( std::vector< BIAS::Vector2< double > > & cPnts )

protected

converts a flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates to a control point vector with x,y-coordinates

Parameters

[out] cPnts control point vector with x,y-coordinates

Definition at line 781 of file ContourDetectorBSpline.cpp.

template<class StorageType >

void ContourDetectorBSpline::QToCPnts_	(	const BIAS::Vector< double > &	Q,
		std::vector< BIAS::Vector2< double > > &	cPnts
	)

protected

converts a flat control point vector Q whose first entries are x-coordinates and last entries are y-coordinates to a control point vector with x,y-coordinates

Parameters

[in]	Q	vector of control points; first entries are x-coordinates; last entries are y-coordinates
[out]	cPnts	control point vector with x,y-coordinates

Definition at line 793 of file ContourDetectorBSpline.cpp.

References TNT::Vector< T >::size().

template<class StorageType >

void ContourDetectorBSpline::Rotate ( const double & angle )

rotates curve by angle (rad)

Parameters

[in] angle rotation angle

Rotates curve by angle. Curve must have its origin at zero! There is no test!

Definition at line 399 of file ContourDetectorBSpline.cpp.

References BIAS::Matrix2x2< T >::Mult().

template<class StorageType >

bool ContourDetectorBSpline::Save ( const std::string & filename )

saves all values which are needed to reinitialise the object to a binary file

Parameters

[in] filename name of destination file

Definition at line 261 of file ContourDetectorBSpline.cpp.

References BIAS::ContourBSplineType::Cluster.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::Scale ( const double & scale )

inline

scales curve isotropic

Parameters

[in] scale scaling factor

Scales the curve by factor "scale". Curve must have its origin at zero! There is no test!

Definition at line 1219 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::ScaleX ( const double & scale )

inline

scales curve in x-direction (horizontally)

Parameters

[in] scale scaling factor

Scales the curve by factor "scale" horizontally. Curve must have its origin at zero! There is no test!

Definition at line 1229 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::ScaleY ( const double & scale )

inline

scales curve in y-direction (vertically)

Parameters

[in] scale scaling factor

Scales the curve by factor "scale" vertically. Curve must have its origin at zero! There is no test!

Definition at line 1239 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetControlPoints ( const std::vector< BIAS::Vector2< double > > & controlPoints )

inline

sets new control points

Parameters

[in] controlPoints vector of control points with x,y-coordinates

Definition at line 1191 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetControlPoints ( const BIAS::Vector< double > & Q )

inline

sets new control points

Parameters

[in] Q vector of control points consisting of vector of x-coordinates and of vector of y-coordinates Q= (x) vector of x-coordinates (y) vector of y-coordinates

Definition at line 1200 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetDrawSamples ( unsigned int samples )

inline

sets the amount of sample points used to draw the B-Spline curve.

Parameters

[in] samples amount of samples used to draw curve

Sets amount of samples which are used to draw the curve. Distance between sample points gets computed (sample width). the sample width is equidistant in sampling the basis of the curve, but not in sampling the curve points!

Definition at line 1403 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetDrawSampleWidth ( double width )

inline

sets the amount of sample points used to draw the B-Spline curve.

Parameters

[in] width distance of sample points along the curve (<1.0)

Computes the amount of samples which are used to draw the curve. These sample points have distance "width". "width" should be considerably <1.0. the sample width is equidistant in sampling the basis of the curve, but not in sampling the curve points!

Definition at line 1393 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetFitSamples ( unsigned int samples )

inline

sets the amount of samples used to search features along the curve in fitting algorithm.

Parameters

[in] samples amount of samples used to search features

Sets amount of samples which are used to search features along the curve in the fitting algorithm. Distance between sample points gets computed (sample width). the sample width is equidistant in sampling the basis of the curve, but not in sampling the curve points!

Definition at line 1383 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetFitSampleWidth ( double width )

inline

sets the amount of samples used to search features along the curve in fitting algorithm.

Parameters

[in] width distance of sample points along the curve (<1.0)

Computes the amount of samples which are used to search features along the curve in the fitting algorithm. These sample points have distance "width". "width" should be considerably <1.0. the sample width is equidistant in sampling the basis of the curve, but not in sampling the curve points!

Definition at line 1373 of file ContourDetectorBSpline.hh.

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetShapeSpaceEuclidian ( )

inline

sets spape-space for regularised fitting to euclidian similarities

Read book "Active Contours" chapter 6 for details.

Definition at line 1285 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceEuclidian().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetShapeSpaceIdentity ( )

inline

sets spape-space for regularised fitting to identity

Read book "Active Contours" chapter 6 for details. If youre not sure which shape-space to set... set this.

Definition at line 1275 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceIdentity().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetShapeSpaceMatrix ( BIAS::Matrix< double > & shapeSpace )

inline

sets spape-space for regularised fitting

Parameters

[in] shapeSpace matrix of shape-space

Read book "Active Contours" chapter 6 for details.

Definition at line 1264 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceMatrix().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetShapeSpacePlanarAffin ( )

inline

sets spape-space for regularised fitting to planar affine transformations

Read book "Active Contours" chapter 6 for details.

Definition at line 1295 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpacePlanarAffin().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetSubShapeSpaceEuclidian ( )

inline

set invariant shape-space for regularised fitting to euclidian similarities

Read book "Active Contours" chapter 6 for details.

Definition at line 1326 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceEuclidian().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetSubShapeSpaceIdentity ( )

inline

set invariant shape-space for regularised fitting to identity

Read book "Active Contours" chapter 6 for details.

Definition at line 1316 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceIdentity().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetSubShapeSpaceMatrix ( BIAS::Matrix< double > & subShapeSpace )

inline

set invariant shape-space for regularised fitting

Parameters

[in] subShapeSpace matrix of invariant shape-space

Read book "Active Contours" chapter 6 for details.

Definition at line 1305 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpaceMatrix().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetSubShapeSpacePlanarAffin ( )

inline

set invariant shape-space for regularised fitting to planar affine transformation

Read book "Active Contours" chapter 6 for details.

Definition at line 1336 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetShapeSpacePlanarAffin().

template<class StorageType >

void BIAS::ContourDetectorBSpline< StorageType >::SetSubShapeSpaceZero ( )

inline

set invariant shape-space for regularised fitting to zero

If you set invariant shape-space matrix to zero, you have to set the normal shape-space before (SetShapeSpace...(...))!

Read book "Active Contours" chapter 6 for details.

Definition at line 1346 of file ContourDetectorBSpline.hh.

References BIAS::ContourBSplineShapeMatrix::SetSubShapeSpaceZero().

template<class StorageType >

void ContourDetectorBSpline::TestInit_	(	const unsigned int	order,
		const ContourBSplineType::Type	bType,
		const std::vector< unsigned int > &	mPnts,
		const BIAS::Vector< double > &	Q
	)

protected

tests if B-Spline curve with given parameter can get initialized.

Parameters

[in]	order	order of B-Spline curve
[in]	bType	type of B-Spline curve (Open or Closed)
[in]	mPnts	vector of multiplicities - determines which control points are modelled as edges
[in]	Q	vector of control points

Test if B-Spline curve with given parameter can get initialised. Most important test if mPnts is correct. That means if edges could be generated in these control points.

Definition at line 1205 of file ContourDetectorBSpline.cpp.

References BIAS::ContourBSplineType::Closed, BIAS::ContourBSplineType::Open, BIAS::Vector< T >::Size(), and TNT::Vector< T >::size().