boofcv.alg.feature.describe

Class DescribePointSift<Deriv extends boofcv.struct.image.ImageGray>

java.lang.Object

boofcv.alg.feature.describe.DescribeSiftCommon

boofcv.alg.feature.describe.DescribePointSift<Deriv>

public class DescribePointSift<Deriv extends boofcv.struct.image.ImageGray>
extends DescribeSiftCommon

A faithful implementation of the SIFT descriptor.

The descriptor is computed inside of a square grid which is scaled and rotated. Each grid cell is composed of a square sub-region. If the sub-region is 4x4 and the outer grid is 5x5 then a total area of size 20x20 is sampled. For each sub-region a histogram with N bins of orientations is computed. Orientation from each sample point comes from the image's spacial derivative. If the outer grid is 4x4 and the histogram N=8, then the total descriptor will be 128 elements.

When a point is sample, its orientation (-pi to pi) and magnitude sqrt(dx**2 + dy**2) are both computed. A contribution from this sample point is added to the entire descriptor and weighted using trilinear interpolation (outer grid x-y coordinate, and orientation bin), Gaussian distribution centered at key point location, and the magnitude.

There are no intentional differences from the paper. However the paper is ambiguous in some places.

• Interpolation method for sampling image pixels isn't specified. Nearest-neighbor is assumed and that's what VLFeat uses too.
• Size of sample region. Oddly enough, I can't find this very important parameter specified anywhere. The suggested value comes from empirical testing.

[1] Lowe, D. "Distinctive image features from scale-invariant keypoints". International Journal of Computer Vision, 60, 2 (2004), pp.91--110.

Field Summary

Fields inherited from class boofcv.alg.feature.describe.DescribeSiftCommon

gaussianWeight, histogramBinWidth, maxDescriptorElementValue, numHistogramBins, widthGrid, widthSubregion

Constructor Summary

Constructors

Constructor and Description
DescribePointSift(int widthSubregion, int widthGrid, int numHistogramBins, double sigmaToPixels, double weightingSigmaFraction, double maxDescriptorElementValue, java.lang.Class<Deriv> derivType) Configures the descriptor.

Method Summary

Modifier and Type	Method and Description
void	process(double c_x, double c_y, double sigma, double orientation, TupleDesc_F64 descriptor) Computes the SIFT descriptor for the specified key point
void	setImageGradient(Deriv derivX, Deriv derivY) Sets the image spacial derivatives.

Methods inherited from class boofcv.alg.feature.describe.DescribeSiftCommon

createGaussianWeightKernel, getCanonicalRadius, getDescriptorLength, normalizeDescriptor, trilinearInterpolation

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

DescribePointSift

public DescribePointSift(int widthSubregion,
                         int widthGrid,
                         int numHistogramBins,
                         double sigmaToPixels,
                         double weightingSigmaFraction,
                         double maxDescriptorElementValue,
                         java.lang.Class<Deriv> derivType)

Configures the descriptor.

Parameters:

widthSubregion - Width of sub-region in samples. Try 4

widthGrid - Width of grid in subregions. Try 4.

numHistogramBins - Number of bins in histogram. Try 8

sigmaToPixels - Conversion of sigma to pixels. Used to scale the descriptor region. Try 1.5 ??????

weightingSigmaFraction - Sigma for Gaussian weighting function is set to this value * region width. Try 0.5

maxDescriptorElementValue - Helps with non-affine changes in lighting. See paper. Try 0.2

Method Detail

setImageGradient

public void setImageGradient(Deriv derivX,
                             Deriv derivY)

Sets the image spacial derivatives. These should be computed from an image at the appropriate scale in scale-space.

Parameters:

derivX - x-derivative of input image

derivY - y-derivative of input image

process

public void process(double c_x,
                    double c_y,
                    double sigma,
                    double orientation,
                    TupleDesc_F64 descriptor)

Computes the SIFT descriptor for the specified key point

Parameters:

c_x - center of key point. x-axis

c_y - center of key point. y-axis

sigma - Computed sigma in scale-space for this point

orientation - Orientation of keypoint in radians

descriptor - (output) Storage for computed descriptor. Make sure it's the appropriate length first