Applies Gabor filter operation on images. More...

#include <GaborOp.h>

Inheritance diagram for icl::filter::GaborOp:

Public Member Functions
	GaborOp ()
	creates an empty GaborOp
	GaborOp (const utils::Size &kernelSize, std::vector< icl32f > lambdas, std::vector< icl32f > thetas, std::vector< icl32f > psis, std::vector< icl32f > sigmas, std::vector< icl32f > gammas)
	creates a new gabor op with given kernel size and parameters
	~GaborOp ()
void	setKernelSize (const utils::Size &size)
	sets the current kernel size
void	addLambda (float lambda)
	add a new lambda value
void	addTheta (float theta)
	add a new theta value
void	addPsi (float psi)
	add a new psi value
void	addSigma (float sigma)
	add a new sigma value
void	addGamma (float gamma)
	add a new gamma value
void	updateKernels ()
	update the current kernels by currently possible value combinations
virtual void	apply (const core::ImgBase poSrc, core::ImgBase *ppoDst)
	apply all filters to an image
std::vector< icl32f >	apply (const core::ImgBase *poSrc, const utils::Point &p)
	apply all filters to an image at a specific position
const std::vector< core::Img32f > &	getKernels () const
	returns all currently created kernels
Static Public Member Functions
static core::Img32f *	createKernel (const utils::Size &size, float lambda, float theta, float psi, float sigma, float gamma)
	static function to create a gabor kernel by given gabor parameters
Private Attributes
std::vector< icl32f >	m_vecLambdas
std::vector< icl32f >	m_vecThetas
std::vector< icl32f >	m_vecPsis
std::vector< icl32f >	m_vecSigmas
std::vector< icl32f >	m_vecGammas
std::vector< core::Img32f >	m_vecKernels
std::vector< core::ImgBase * >	m_vecResults
utils::Size	m_oKernelSize

Detailed Description

Applies Gabor filter operation on images.

General Information

A short introduction to Gabor filters can be found at Wikipedia: A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property, the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function.

$g(x,y;\lambda,\theta,\psi,\sigma,\gamma)=\exp(-\frac{x'^2+\gamma^2y'^2}{2\sigma^2})\cos(2\pi\frac{x'}{\lambda}+\psi)$

where

$x' = x \cos\theta + y \sin\theta\,$

and

$y' = -x \sin\theta + y \cos\theta\,$

In this equation, $\lambda$ represents the wavelength of the cosine factor, $\theta$ represents the orientation of the normal to the parallel stripes of a Gabor function in degrees, $\psi$ is the phase offset in degrees, and $\gamma$ is the spatial aspect ratio, and specifies the ellipticity of the support of the Gabor function.

Gabor filters are directly related to Gabor wavelets, since they can be designed for number of dilations and rotations. However, in general, expansion is not applied for Gabor wavelets, since this requires computation of biorthogonal wavelets, which may be very time-consuming. Therefore, usually, a filter bank consisting of Gabor filters with various scales and rotations is created. The filters are convolved with the signal, resulting in a so-called Gabor space. This process is closely related to processes in the primary visual cortex. The Gabor space is very useful in e.g., image processing applications such as iris recognition. Relations between activations for a specific spatial location are very distinctive between objects in an image. Furthermore, important activations can be extracted from the Gabor space in order to create a sparse object representation (cite http://en.wikipedia.org/wiki/Gabor_filter).

The GaborOp class

The GaborOp class provides basic functionalities for applying Gabor filters on Images. To achieve optimal performance, it wraps the ConvolutionOp class to realize the internal image convolution operations. Determined by a set op input parameters, it internally creates a filter bank that caches all gabor masks. In contrast to other filters, it knows two modes:

whole image mode in this mode, the filter bank image applied on the whole input image, and an output image is created with one channel for each filter. This mode works essentially like all other UnaryOps.
specified center mode here, the filters are applied not on all image locations, but on some well defined image locations. The result is not an image, but a matrix of filter responses, where the matrix's x index references the convolution center, and the y index then defines the filter index on this location. (... some more detail here!)

The GaborOp class provides functionalities for the creation of Gabor-Filter kernels, as well as for applying gabor filter operation on images. As mentioned above, in contrast to other convolution operations, Gabor filters are often applied as so called Gabor-Jets at some specified image locations only. A Gabor-Jet complies a stack of gabor kernels that are created by some methodical variation of one, some or all gabor mask parameters.
Each GaborOp object provide function to create a gabor jet internally, whereas in the easiest case, there is only one value for each parameter, an consequently, only a single gabor mask is created.
In addition to the parameters mentioned in the formula above, the size of the created gabor kernels must be set, and the parameter values must be adapted to to it. In the following, each parameter is explained again, but this time with respect to its underlying effect for a kernel size of KWxKH.

$\lambda$ wave length of pixels (e.g. if set to KW, the wave will oscillate exactly once for direction ( $\theta=0$ )
$\theta$ orientation of the wave (0-> wave direction is 3 o'clock, $\frac{\pi}{2}$ -> wave direction is 12 o'clock)
$\psi$ phase shift of the wave
$\sigma$ std.deviation of the Gaussian multiplied with the wave (in pixels)
$\gamma$ aspect-ratio of the Gaussian

Constructor & Destructor Documentation

icl::filter::GaborOp::GaborOp ( )

creates an empty GaborOp

icl::filter::GaborOp::GaborOp	(	const utils::Size &	kernelSize,
		std::vector< icl32f >	lambdas,
		std::vector< icl32f >	thetas,
		std::vector< icl32f >	psis,
		std::vector< icl32f >	sigmas,
		std::vector< icl32f >	gammas
	)

creates a new gabor op with given kernel size and parameters

The Gabor-Jet internally created consist of one convolution kernel for each possible combination of the parameter values. E.g. if the parameters are:

lambdas = {5}
thetas = {1,2}
psis = {0}
sigmas = {100,200}
and gammas = {1]

the gabor jet consist of 4 convolution kernels with fixed params $\lambda=5$ , $\psi=0$ , $\gamma=1$ and variable params

$\theta=1$ and $\sigma=100$
$\theta=2$ and $\sigma=100$
$\theta=1$ and $\sigma=200$
$\theta=2$ and $\sigma=200$

icl::filter::GaborOp::~GaborOp ( )

Member Function Documentation

void icl::filter::GaborOp::addGamma ( float gamma )

add a new gamma value

void icl::filter::GaborOp::addLambda ( float lambda )

add a new lambda value

void icl::filter::GaborOp::addPsi ( float psi )

add a new psi value

void icl::filter::GaborOp::addSigma ( float sigma )

add a new sigma value

void icl::filter::GaborOp::addTheta ( float theta )

add a new theta value

virtual void icl::filter::GaborOp::apply	(	const core::ImgBase *	poSrc,
		core::ImgBase **	ppoDst
	)		`[virtual]`

apply all filters to an image

The output image gets as many channels as kernels could be created by combining given parameters. Channels c of ppoDst is complies the convolution result of the c-th kernel.

Implements icl::filter::UnaryOp.

std::vector<icl32f> icl::filter::GaborOp::apply	(	const core::ImgBase *	poSrc,
		const utils::Point &	p
	)

apply all filters to an image at a specific position

The result vector contains the filter-response for all kernels

static core::Img32f* icl::filter::GaborOp::createKernel	(	const utils::Size &	size,
		float	lambda,
		float	theta,
		float	psi,
		float	sigma,
		float	gamma
	)		`[static]`