boofcv.alg.feature.detect.intensity

Interface GradientCornerIntensity<T extends ImageSingleBand>

All Superinterfaces:

FeatureIntensity<T>

All Known Subinterfaces:

HarrisCornerIntensity<T>, ShiTomasiCornerIntensity<T>

All Known Implementing Classes:

ImplHarrisCorner_F32, ImplHarrisCorner_S16, ImplHarrisCornerWeighted_F32, ImplHarrisCornerWeighted_S16, ImplShiTomasiCorner_F32, ImplShiTomasiCorner_S16, ImplShiTomasiCornerWeighted_F32, ImplShiTomasiCornerWeighted_S16, ImplSsdCorner_F32, ImplSsdCorner_S16, ImplSsdCornerBase, ImplSsdCornerNaive, ImplSsdCornerWeighted_F32, ImplSsdCornerWeighted_S16

public interface GradientCornerIntensity<T extends ImageSingleBand>
extends FeatureIntensity<T>

Several different types of corner detectors [1,2] all share the same initial processing steps. First a 2 by 2 deformation matrix D = [ Ixx , Ixy ; Iyx , Iyy] is computed around each pixel. D is computed by summing up the product of each pixel's gradient inside of a window. Next how corner like each pixel is computed using the information in the deformation matrix. In the final step where each of these techniques differ.

Ixx = Sum dX*dX
Ixy = Iyx = Sum dX*dY
Iyy = Sum dY*dY
where the Sum is the sum across all the pixels within a rectangular window, and [dX,dY] is a pixel's gradient.

Alternative implementations can consider a weighted window around the pixel. By considering only a uniform set of weights several optimizations are possible. The runtime is independent of the window size and can be very efficiently computed.

[1] Jianbo Shi and Carlo Tomasi. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, 1994
[2] E.R. Davies, "Machine Vision Theory Algorithms Practicalities," 3rd ed. 2005

Author:

Peter Abeles

Method Summary

Methods

Modifier and Type	Method and Description
void	process(T derivX, T derivY, ImageFloat32 intensity) Computes feature intensity image.

Methods inherited from interface boofcv.alg.feature.detect.intensity.FeatureIntensity

getIgnoreBorder, getRadius

Method Detail

process

void process(T derivX,
           T derivY,
           ImageFloat32 intensity)

Computes feature intensity image.

Parameters:

derivX - Image derivative along the x-axis.

derivY - Image derivative along the y-axis.

intensity - Output intensity image