Processing math: 100%
ImageDev

TensorVoting2d

Strengthens local features in a two-dimensional image according to their consistency with a model of smooth curves.

Access to parameter description

The purpose of this algorithm is to fill gaps inside features appearing in a score image along their orientations given by a vector image. The structural information contained in the feature image is propagated through a voting field.

The voting field used assumes that the best connection between two points with one orientation imposes a circular arc that decays with the radius according to a Gaussian function.
The voting field size (that is, the maximum length of gaps to fill) is defined by a scale parameter.

This voting field can be illustrated on the unit sphere:
<b> Figure 1.</b> Example of a stick voting field
Figure 1. Example of a stick voting field


Different outputs can be computed by this filter and can be enabled or not by a selection parameter.
Only the outputs necessary to the application have to be selected in order to minimize memory and computation time usage.

This filter can provide a spectral image output where each channel represents a tensor component set in the following order Ixx, Ixy, Iyy.

The saliency, ballness, and orientation vector images can be extracted from this tensor image. These metrics are defined as follows: Where:
This method is referenced by Franken and van Almsick publication:
E.Franken, M.van Almsick, P.Rongen, L.Florack, B.ter Haar Romeny. "An efficient method for tensor voting using steerable filters". Computer Vision - ECCV 2006, Springer, pp. 228-240, 2006.

See also

Function Syntax

This function returns a TensorVoting2dOutput structure containing outputTensorImage, outputVectorImage, outputSaliencyImage and outputBallnessImage. 
// Output structure of the tensorVoting2d function.
struct TensorVoting2dOutput
{
    /// The tensor output corresponding to the strengthened image.
    std::shared_ptr< iolink::ImageView > outputTensorImage;
    /// The orientation vector output image, defining the main orientation of the output tensor.
    std::shared_ptr< iolink::ImageView > outputVectorImage;
    /// The saliency output image representing the anisotropy of the structures.
    std::shared_ptr< iolink::ImageView > outputSaliencyImage;
    /// The ballness output image representing the isotropy of the structures.
    std::shared_ptr< iolink::ImageView > outputBallnessImage;
};

// Function prototype
TensorVoting2dOutput
tensorVoting2d( std::shared_ptr< iolink::ImageView > inputFeatureImage,
                std::shared_ptr< iolink::ImageView > inputVectorImage,
                double scale,
                int32_t outputSelection,
                std::shared_ptr< iolink::ImageView > outputTensorImage = NULL,
                std::shared_ptr< iolink::ImageView > outputVectorImage = NULL,
                std::shared_ptr< iolink::ImageView > outputSaliencyImage = NULL,
                std::shared_ptr< iolink::ImageView > outputBallnessImage = NULL );

Class Syntax

Parameters

Parameter Name Description Type Supported Values Default Value
input
inputFeatureImage
The feature input image. It can be produced by the EigenvaluesToStructureness2d algorithm.
This image must have only one spectral channel. 		Image Binary, Label or Grayscale nullptr
input
inputVectorImage
The vector input image indicating the orientation of the feature to strengthen. It can be produced by the EigenDecomposition2d algorithm. When used with an edge detector such as an Hessian matrix, the second eigenvector has to be selected since the first eigenvector is orthogonal to the structures.
This image must have the same size as the feature input image and 2 spectral channels. Each vector is used to define an orientation and its norm is ignored. 		Image Binary, Label, Grayscale or Multispectral nullptr
input
scale
The scale parameter acting on the gap length filled by the tensor voting algorithm. It is expressed in pixels. Float64 >0 5
input
outputSelection
The output images to be computed.
TENSOR Enable the computation of the tensor output image. Associated value = 1.
ORIENTATION_VECTOR Enable the computation of the orientation vector output image. Associated value = 2.
SALIENCY Enable the computation of the saliency output image. Associated value = 4.
BALLNESS Enable the computation of the ballness output image. Associated value = 8.
MultipleChoice SALIENCY
output
outputTensorImage
The tensor output corresponding to the strengthened image.
The spatial dimensions, calibration and interpretation of the output image are forced to the same values as the feature input. Type is forced to float. 		Image nullptr
output
outputVectorImage
The orientation vector output image, defining the main orientation of the output tensor.
The spatial dimensions, calibration, and interpretation of the output image are forced to the same values as the feature input. The size of the spectral serie component is forced to 2. Each vector defines the main orientation of the output tensor and its norm is equal to the saliency. Type is forced to float. 		Image nullptr
output
outputSaliencyImage
The saliency output image representing the anisotropy of the structures.
The spatial dimensions, calibration, and interpretation of the output image are forced to the same values as the feature input. Type is forced to float. 		Image nullptr
output
outputBallnessImage
The ballness output image representing the isotropy of the structures.
The spatial dimensions, calibration, and interpretation of the output image are forced to the same values as the feature input. Type is forced to float. 		Image nullptr

Object Examples

auto retina_structureness = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "retina_structureness.vip" );
auto retina_orientation_vector = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "retina_orientation_vector.vip" );

TensorVoting2d tensorVoting2dAlgo;
tensorVoting2dAlgo.setInputFeatureImage( retina_structureness );
tensorVoting2dAlgo.setInputVectorImage( retina_orientation_vector );
tensorVoting2dAlgo.setScale( 5 );
tensorVoting2dAlgo.setOutputSelection( 4 );
tensorVoting2dAlgo.execute();

std::cout << "outputTensorImage:" << tensorVoting2dAlgo.outputTensorImage()->toString();
std::cout << "outputVectorImage:" << tensorVoting2dAlgo.outputVectorImage()->toString();
std::cout << "outputSaliencyImage:" << tensorVoting2dAlgo.outputSaliencyImage()->toString();
std::cout << "outputBallnessImage:" << tensorVoting2dAlgo.outputBallnessImage()->toString();

Function Examples

auto retina_structureness = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "retina_structureness.vip" );
auto retina_orientation_vector = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "retina_orientation_vector.vip" );

auto result = tensorVoting2d( retina_structureness, retina_orientation_vector, 5, 4 );

std::cout << "outputTensorImage:" << result.outputTensorImage->toString();
std::cout << "outputVectorImage:" << result.outputVectorImage->toString();
std::cout << "outputSaliencyImage:" << result.outputSaliencyImage->toString();
std::cout << "outputBallnessImage:" << result.outputBallnessImage->toString();