Functions
cvbres_t	AddGaussNoise (IMG ImgIn, double Mean, double StdDev, long Seed, IMG &ImgOut)
	Add gaussian distributed noise to the image. More...

cvbres_t	AddUniformNoise (IMG ImgIn, double Lowest, double Highest, long Seed, IMG &ImgOut)
	Add uniformly distributed noise to the image. More...

cvbres_t	CreateFilterTestImage (long Width, long Height, long Dimension, long BitsPerPixel, cvbbool_t Signed, cvbbool_t Float, IMG &ImgOut)
	Creates an image with concentric circles with a frequency that increases as the radius of the circles increases. More...

cvbres_t	CreateGreyRamp (long Width, long Height, long Dimension, long BitsPerPixel, cvbbool_t Signed, cvbbool_t Float, double Offset, double Slope, cvbbool_t AxisHorizontal, cvbbool_t AxisVertical, IMG &ImgOut)
	Creates an n-channel image that can be used as a test image to examine the effect of applying different image processing functions. More...

cvbres_t	DistanceTransformation (IMG ImgIn, long Index, TFilterMask MaskSize, TDistanceNorm Norm, IMG &ImgOut)
	Calculates the distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image. More...

cvbres_t	EuclideanDistanceTransform (IMG ImgIn, cvbdim_t Index, IMG &ImgOut)
	Calculates the Euclidean distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image. More...

cvbres_t	FastMarchingDistanceTransform (IMG ImgIn, cvbdim_t Index, float Radius, IMG &ImgOut)
	Calculates the distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image using the fast marching method. More...

cvbres_t	InitializeImageRect (IMG ImgIn, long left, long top, long right, long bottom, double *ValuesIn)
	Initialises the pixels in a rectangular area of interest with values from an array. More...

cvbres_t	LocalEigenValuesAndVectors (IMG ImgIn, long Index, TFilterMask DerivatorSize, TFilterMask BlurSize, IMG &ImgLambda1, IMG &ImgLambda2, IMG &ImgEV1X, IMG &ImgEV1Y, IMG &ImgEV2X, IMG &ImgEV2Y)
	Computes, for every pixel in the input image, both eigen vectors and eigen values of the matrix More...

cvbres_t	LocalMinEigenValues (IMG ImgIn, long Index, TFilterMask DerivatorSize, TFilterMask BlurSize, IMG &ImgLambdaMin)
	Computes, for every pixel in the input image the minimum eigen vector of the matrix More...

cvbres_t	PyramidDownSample (IMG ImgIn, IMG &ImgOut)
	Down-samples an image in the sense of a Gaussian pyramid. More...

cvbres_t	PyramidUpSample (IMG ImgIn, IMG &ImgOut)
	Up-samples an image in the sense of a Gaussian pyramid. More...

cvbres_t	SwapRGBChannels (IMG ImgIn, long Order[3], IMG &ImgOut)
	Changes the plane order of a multi-planar image in memory. More...

Detailed Description

Function Documentation

◆ AddGaussNoise()

cvbres_t AddGaussNoise	(	IMG	ImgIn,
		double	Mean,
		double	StdDev,
		long	Seed,
		IMG &	ImgOut
	)

Add gaussian distributed noise to the image.

This function generates random noise with Gaussian distribution that has the mean value 
and standard deviation and adds it to a source image. The resulting pixel values that exceed the
image data range are saturated to the respective data-range limits. To obtain an image which contains pure noise
with Gaussian distribution, call \ref AddGaussNoise using a source image with zero (black) data as input. \n\n
Sample: Image with added gaussian noise with mean = 32 and stddev = 32:
<table border="0">
<tr> <td> <img class="ToDisplay" src="Bracket.gif" align="left"  alt="Bracket original"> </td>

Bracket Gauss

Parameters

[in]	ImgIn	Handle of image object.
[in]	Mean	Mean of the gaussian distribution.
[in]	StdDev	Standard deviation of the gaussian distribution.
[in]	Seed	Initial seed value for the pseudo-random number generator.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: AddUniformNoise

◆ AddUniformNoise()

cvbres_t AddUniformNoise	(	IMG	ImgIn,
		double	Lowest,
		double	Highest,
		long	Seed,
		IMG &	ImgOut
	)

Add uniformly distributed noise to the image.

This function generates noise with uniform distribution over the range [low, high] and adds them to a source image. \n
The resulting pixel values that exceed the image data range are saturated to the respective data-range limits.
To obtain an image which contains pure noise with uniform distribution, 
call \ref AddUniformNoise using a source image with zero (black) data as input.  \n\n
Sample: Image with added uniform noise between 0 and 64:
<table border="0">
<tr> <td> <img class="ToDisplay" src="Bracket.gif" align="left"  alt="Bracket original"> </td>

Bracket Uniform

Parameters

[in]	ImgIn	Handle of image object.
[in]	Lowest	Lower bound for the uniformly distributed noise values.
[in]	Highest	Upper bound for the uniformly distributed noise values.
[in]	Seed	Initial seed value for the pseudo-random number generator.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: AddGaussNoise

◆ CreateFilterTestImage()

cvbres_t CreateFilterTestImage	(	long	Width,
		long	Height,
		long	Dimension,
		long	BitsPerPixel,
		cvbbool_t	Signed,
		cvbbool_t	Float,
		IMG &	ImgOut
	)

Creates an image with concentric circles with a frequency that increases as the radius of the circles increases.

Such an image may be useful to test the characteristics (especially the anisotropy) of an image filter:
<table border="0">
<tr> <td> <img class="ToDisplay" src="FilterTest384x384.gif" align="left"  alt="Test filter image"> </td>

Parameters

[in]	Width	Width of the image to be created.
[in]	Height	Height of the image to be created.
[in]	Dimension	Number of planes in the image to be created (1 for monochrome, 3 for RGB image...).
[in]	BitsPerPixel	Number of bits per pixel of the output image. Attention: Only 8 bits, 16 bits and 32 bits are allowed!
[in]	Signed	`TRUE` indicates a signed image, `FALSE` creates an unsigned image.
[in]	Float	`TRUE` creates a floating point image, `FALSE` an image with integer values.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

◆ CreateGreyRamp()

cvbres_t CreateGreyRamp	(	long	Width,
		long	Height,
		long	Dimension,
		long	BitsPerPixel,
		cvbbool_t	Signed,
		cvbbool_t	Float,
		double	Offset,
		double	Slope,
		cvbbool_t	AxisHorizontal,
		cvbbool_t	AxisVertical,
		IMG &	ImgOut
	)

Creates an n-channel image that can be used as a test image to examine the effect of applying different image processing functions.

The destination image pixel values are computed according to one of the following formula: \n\n
Horizontal-only ramp:\n
&nbsp; &nbsp; dst(x,y) = offset + slope * x\n
Vertical-only ramp:\n
&nbsp; &nbsp; dst(x,y) = offset + slope * y \n
Horizontal and vertical ramp: \n
&nbsp; &nbsp; dst(x,y) = offset + slope * x * y
<table border="0">
<tr> <td> <img class="ToDisplay" src="ImageRamp.gif" align="left"  alt="Test range image"> </td>

Attention: The linear transform coefficients offset and slope have floating-point values. The computed pixel values that exceed the image data range are saturated to the respective data-range limits.

Parameters

[in]	Width	Width of the image to be created.
[in]	Height	Height of the image to be created.
[in]	Dimension	Number of planes in the image to be created (1 for monochrome, 3 for RGB image...).
[in]	BitsPerPixel	Number of bits per pixel of the output image. Attention: Only 8 bits, 16 bits and 32 bits are allowed!
[in]	Signed	`TRUE` indicates a signed image, `FALSE` creates an unsigned image.
[in]	Float	`TRUE` creates a floating point image, `FALSE` an image with integer values.
[in]	Offset	Gray-wedge offset value.
[in]	Slope	Gray-wedge slope coefficient.
[in]	AxisHorizontal	Specifies the horizontal direction of the image intensity ramp.
[in]	AxisVertical	Specifies the vertical direction of the image intensity ramp.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

◆ DistanceTransformation()

cvbres_t DistanceTransformation	(	IMG	ImgIn,
		long	Index,
		TFilterMask	MaskSize,
		TDistanceNorm	Norm,
		IMG &	ImgOut
	)

Calculates the distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image.

The distance is calculated approximatively using a set of principal distances that is governed by the MaskSize parameter and the distance norm. \n\n
Example: Distance transformation on Bracket.bmp.
To get a suitable input image for the distance transformation, the inverse of the canny edge filter image is used:
<table border="0">
<tr> <td> <img class="ToDisplay" src="Bracket.gif" align="left"  alt="Bracket original"> </td>

Bracket Canny Negative

Bracket Distance

Input image

Inverse of Canny edge filtered image

Distance transformation with L2 norm and 3x3 mask

Parameters

[in]	ImgIn	Handle of image object.
[in]	Index	Plane index in the image to work on.
[in]	MaskSize	Size of the distance approximator to be used. Possible sizes are FM_3x3 and FM_5x5.
[in]	Norm	Norm to be used to build the approximator. Allowable values are DN_LInf, DN_L1 and DN_L2.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: #TDistanceNorm, #TFilterMask, EuclideanDistanceTransform

◆ EuclideanDistanceTransform()

cvbres_t EuclideanDistanceTransform	(	IMG	ImgIn,
		cvbdim_t	Index,
		IMG &	ImgOut
	)

Calculates the Euclidean distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image.

Parameters

[in]	ImgIn	Handle of image object.
[in]	Index	Plane index in the image to work on.
[out]	ImgOut	Handle of result image. The result image contains floating point values.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: DistanceTransformation

◆ FastMarchingDistanceTransform()

cvbres_t FastMarchingDistanceTransform	(	IMG	ImgIn,
		cvbdim_t	Index,
		float	Radius,
		IMG &	ImgOut
	)

Calculates the distance to the closest pixel in the source image with value zero for all non-zero pixels in the input image using the fast marching method.

Parameters

[in]	ImgIn	Handle of image object.
[in]	Index	Plane index in the image to work on.
[in]	Radius	Radius of the neighborhood of the marked area. If the Radius is positive, then the FMM (fast marching method) distance with the negative sign is calculated in the Euclidean radius neighborhood.
[out]	ImgOut	Handle of result image. The result image contains floating point values.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: DistanceTransformation

◆ InitializeImageRect()

cvbres_t InitializeImageRect	(	IMG	ImgIn,
		long	left,
		long	top,
		long	right,
		long	bottom,
		double *	ValuesIn
	)

Initialises the pixels in a rectangular area of interest with values from an array.

Parameters

[in]	ImgIn	Handle of image object.
[in]	left	Left edge of the area of interest.
[in]	top	Top edge of the area of interest.
[in]	right	Right edge of the area of interest.
[in]	bottom	Bottom edge of the area of interest.
[in]	ValuesIn	Buffer with intensity values with which to initialize the selected area of interest in the image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: Image Dll function InitializeImageArea
Areas and rectangles

◆ LocalEigenValuesAndVectors()

cvbres_t LocalEigenValuesAndVectors	(	IMG	ImgIn,
		long	Index,
		TFilterMask	DerivatorSize,
		TFilterMask	BlurSize,
		IMG &	ImgLambda1,
		IMG &	ImgLambda2,
		IMG &	ImgEV1X,
		IMG &	ImgEV1Y,
		IMG &	ImgEV2X,
		IMG &	ImgEV2Y
	)

Computes, for every pixel in the input image, both eigen vectors and eigen values of the matrix

Eigen matrix

D stands for the derivative operator (it is the Sobel operator that is applied internally here) in x or y direction. Summation occurs over the blur window around a pixel. The output is a set of 6 floating-point images, containing the two eigen values (not sorted by size) and the associated eigen vectors for each pixel of the input image. In cases where the matrix is singular or when one eigen value is much bigger than the other, all 6 output values are set to zero.

This kind of operation is useful when it comes to detecting e. g. curvatures in the image, because the eigen values tend to differ significantly from zero only in regions where the are curved edges. Perfectly straight edges or random noise will result in eigen values close to zero.

Input image


Eigen Value 1	Eigen Vector 1, X component	Eigen Vector 1, Y component


Eigen Value 2	Eigen Vector 2, X component	Eigen Vector 2, Y component

Parameters

[in]	ImgIn	Handle of image object.
[in]	Index	Plane index to work on.
[in]	DerivatorSize	Size of the kernel of the Sobel operator to be used for the derivation operations internally. Allowable values are FM_3x3, FM_5x5 .
[in]	BlurSize	Mask size for blurring/summation. Allowable values are FM_3x3, FM_5x5 .
[out]	ImgLambda1	First eigen values for each pixel of the input image. Attention: Note that the pixel format is floating point.
[out]	ImgLambda2	Second eigen values for each pixel of the input image. Attention: Note that the pixel format is floating point.
[out]	ImgEV1X	X-component of the eigen vector associated with the first eigen value for each pixel of the input image. Attention: Note that the pixel format is floating point.
[out]	ImgEV1Y	Y-component of the eigen vector associated with the first eigen value for each pixel of the input image. Attention: Note that the pixel format is floating point.
[out]	ImgEV2X	X-component of the eigen vector associated with the second eigen value for each pixel of the input image. Attention: Note that the pixel format is floating point.
[out]	ImgEV2Y	Y-component of the eigen vector associated with the second eigen value for each pixel of the input image. Attention: Note that the pixel format is floating point.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: TFilterMask, LocalMinEigenValues

◆ LocalMinEigenValues()

cvbres_t LocalMinEigenValues	(	IMG	ImgIn,
		long	Index,
		TFilterMask	DerivatorSize,
		TFilterMask	BlurSize,
		IMG &	ImgLambdaMin
	)

Computes, for every pixel in the input image the minimum eigen vector of the matrix

Eigen matrix

D stands for the derivative operator (it is the Sobel operator that is applied internally here) in x or y direction. Summation occurs over the blur window around a pixel. The output is a floating-point image, containing the smallest of the two eigen values for each pixel of the input image.

This kind of operation is useful when it comes to detecting e. g. curvatures in the image, because the eigen values tend to differ significantly from zero only in regions where the are curved edges. Perfectly straight edges or random noise will result in eigen values close to zero.


Input image	Minimum Eigen Values

Parameters

[in]	ImgIn	Handle of image object.
[in]	Index	Plane index to work on.
[in]	DerivatorSize	Size of the kernel of the Sobel operator to be used for the derivation operations internally. Allowable values are FM_3x3, FM_5x5 .
[in]	BlurSize	Mask size for blurring/summation. Allowable values are FM_3x3, FM_5x5 .
[out]	ImgLambdaMin	Minimum eigen values for each pixel of the input image. Attention: Note that the pixel format is floating point.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: TFilterMask, LocalEigenValuesAndVectors

◆ PyramidDownSample()

cvbres_t PyramidDownSample	(	IMG	ImgIn,
		IMG &	ImgOut
	)

Down-samples an image in the sense of a Gaussian pyramid.

It applies a 5x5 Gaussian Kernel to the image, then down-samples the image by omitting every odd row and column,
producing an output image whose size is halved in each dimension.

Parameters

[in]	ImgIn	Handle of image object.
[out]	ImgOut	Handle of the result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: PyramidUpSample

◆ PyramidUpSample()

cvbres_t PyramidUpSample	(	IMG	ImgIn,
		IMG &	ImgOut
	)

Up-samples an image in the sense of a Gaussian pyramid.

It inserts odd columns and rows filled with 0, then applies a 5x5
Gaussian Kernel multiplied by 4 to the image.

Parameters

[in]	ImgIn	Handle of image object.
[out]	ImgOut	Handle of the result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64

Related Topics:: PyramidDownSample

◆ SwapRGBChannels()

cvbres_t SwapRGBChannels	(	IMG	ImgIn,
		long	Order[3],
		IMG &	ImgOut
	)

Changes the plane order of a multi-planar image in memory.

The change is not virtual - the actual bytes in memory are swapped according to the function parameters. 
The new plane order is determined by the array parameter order.\n\n
For example, if sequence of planes in source image is A, B, C and Order[0]=2, Order[1]=0, Order[2]=1, then the 
order of planes in the destination image will be C, A, B. Some or all components of Order may have the same values.

Parameters

[in]	ImgIn	Handle of image object.
[in]	Order	Sequence of the channels in the destination image. Attention: The size of the array must be 3 and should have o other values than 0, 1 and 2.
[out]	ImgOut	Handle of result image.

Returns: 0 : OK
< 0: an error occurred.

Supported platforms:: Win32
Win64


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Functions

Detailed Description

Function Documentation

◆ AddGaussNoise()

◆ AddUniformNoise()

◆ CreateFilterTestImage()

◆ CreateGreyRamp()

◆ DistanceTransformation()

◆ EuclideanDistanceTransform()

◆ FastMarchingDistanceTransform()

◆ InitializeImageRect()

◆ LocalEigenValuesAndVectors()

◆ LocalMinEigenValues()

◆ PyramidDownSample()

◆ PyramidUpSample()

◆ SwapRGBChannels()