OpenCV  3.3.0-dev
Open Source Computer Vision
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Extended Image Processing

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Functions

void cv::ximgproc::anisotropicDiffusion (InputArray src, OutputArray dst, float alpha, float K, int niters)
 Performs anisotropic diffusian on an image. More...
 
void cv::ximgproc::niBlackThreshold (InputArray _src, OutputArray _dst, double maxValue, int type, int blockSize, double k, int binarizationMethod=BINARIZATION_NIBLACK)
 Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. More...
 
Matx23d cv::PeiLinNormalization (InputArray I)
 Calculates an affine transformation that normalize given image using Pei&Lin Normalization. More...
 
void cv::PeiLinNormalization (InputArray I, OutputArray T)
 
void cv::ximgproc::thinning (InputArray src, OutputArray dst, int thinningType=THINNING_ZHANGSUEN)
 Applies a binary blob thinning operation, to achieve a skeletization of the input image. More...
 

Detailed Description

Function Documentation

§ anisotropicDiffusion()

void cv::ximgproc::anisotropicDiffusion ( InputArray  src,
OutputArray  dst,
float  alpha,
float  K,
int  niters 
)

Performs anisotropic diffusian on an image.

The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation:

\[{\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\]

Suggested functions for c(x,y,t) are:

\[c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\]

or

\[ c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \]

Parameters
srcGrayscale Source image.
dstDestination image of the same size and the same number of channels as src .
alphaThe amount of time to step forward by on each iteration (normally, it's between 0 and 1).
Ksensitivity to the edges
nitersThe number of iterations

§ niBlackThreshold()

void cv::ximgproc::niBlackThreshold ( InputArray  _src,
OutputArray  _dst,
double  maxValue,
int  type,
int  blockSize,
double  k,
int  binarizationMethod = BINARIZATION_NIBLACK 
)

Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired.

The function transforms a grayscale image to a binary image according to the formulae:

  • THRESH_BINARY

    \[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\]

  • THRESH_BINARY_INV

    \[dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\]

    where \(T(x,y)\) is a threshold calculated individually for each pixel.

The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\).

The function can't process the image in-place.

Parameters
_srcSource 8-bit single-channel image.
_dstDestination image of the same size and the same type as src.
maxValueNon-zero value assigned to the pixels for which the condition is satisfied, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.
typeThresholding type, see cv::ThresholdTypes.
blockSizeSize of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on.
kThe user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean.
binarizationMethodBinarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see cv::ximgproc::LocalBinarizationMethods.
See also
threshold, adaptiveThreshold

§ PeiLinNormalization() [1/2]

Matx23d cv::PeiLinNormalization ( InputArray  I)

Calculates an affine transformation that normalize given image using Pei&Lin Normalization.

Assume given image \(I=T(\bar{I})\) where \(\bar{I}\) is a normalized image and \(T\) is an affine transformation distorting this image by translation, rotation, scaling and skew. The function returns an affine transformation matrix corresponding to the transformation \(T^{-1}\) described in [PeiLin95]. For more details about this implementation, please see [PeiLin95] Soo-Chang Pei and Chao-Nan Lin. Image normalization for pattern recognition. Image and Vision Computing, Vol. 13, N.10, pp. 711-723, 1995.

Parameters
IGiven transformed image.
Returns
Transformation matrix corresponding to inversed image transformation

§ PeiLinNormalization() [2/2]

void cv::PeiLinNormalization ( InputArray  I,
OutputArray  T 
)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

§ thinning()

void cv::ximgproc::thinning ( InputArray  src,
OutputArray  dst,
int  thinningType = THINNING_ZHANGSUEN 
)

Applies a binary blob thinning operation, to achieve a skeletization of the input image.

The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.

Parameters
srcSource 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values.
dstDestination image of the same size and the same type as src. The function can work in-place.
thinningTypeValue that defines which thinning algorithm should be used. See cv::ximgproc::ThinningTypes