boofcv.alg.feature.detect.interest

Class SiftDetector

java.lang.Object

boofcv.alg.feature.detect.interest.SiftDetector

Direct Known Subclasses:

CompleteSift

public class SiftDetector
extends java.lang.Object

Implementation of SIFT [1] feature detector. Feature detection is first done by creating the first octave in a scale space. Then the Difference-of-Gaussian (DoG) is computed from sequential scales inside the scale-space. From the DoG images, pixels which are maximums and minimums spatially and with in scale are found. Edges of objects can cause false positives so those are suppressed. The remaining features are interpolated spatially and across scale.

This class is designed so that it can operate as a stand alone feature detector or so that it can be extended to compute feature descriptors too. The advantage of the former is that the scale-space only needs to be constructed once.

Processing Steps

1. Construct first octave of DoG images using SiftScaleSpace
2. For DoG images 1 to N+1, detect features
3. Use NonMaxLimiter to detect features spatially.
4. Check to see if detected features are minimums or maximums in DoG scale space by checking the equivalent 3x3 regions in the DoG images above and below it. isScaleSpaceExtremum(int, int, float, float)
5. Detect false positive edges using trace and determinant from Hessian of DoG image
6. Interpolate feature's (x,y,sigma) coordinate using the peak of a 2nd order polynomial (quadratic). processFeatureCandidate(int, int, float, boolean)

Where N is the number of scale parameters. There are N+3 scale images and N+2 DoG images in an octave.

Edge Detection

Edges can also cause local extremes (false positives) in the DoG image. To remove those false positives an edge detector is proposed by Lowe. The edge detector is turned with the parameter 'r' and a point is considered an edge if the following is true:
Tr²/Det < (r+1)²/r
where Tr and Det are the trace an determinant of a 2x2 hessian matrix computed from the DoG hessian at that point, [dXX,dXY;dYX,dYY]

Deviations from standard SIFT

1. Spatial maximums are not limited to a 3x3 region like they are in the paper. User configurable.
2. Quadratic interpolation is used independently on x,y, and scale axis.
3. What the scale of a DoG image is isn't specified in the paper. Assumed to be the same as the lower indexed scale image it was computed from.

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

Field Summary

Fields

Modifier and Type	Field and Description
protected org.ddogleg.struct.FastQueue<ScalePoint>	detections
protected double	pixelScaleToInput
protected SiftScaleSpace	scaleSpace

Constructor Summary

Constructors

Constructor and Description
SiftDetector(SiftScaleSpace scaleSpace, double edgeR, NonMaxLimiter extractor) Configures SIFT detector

Method Summary

Modifier and Type	Method and Description
protected void	detectFeatures(int scaleIndex) Detect features inside the Difference-of-Gaussian image at the current scale
org.ddogleg.struct.FastQueue<ScalePoint>	getDetections()
protected void	handleDetection(ScalePoint p) Function for handling a detected point.
void	process(boofcv.struct.image.GrayF32 input) Detects SIFT features inside the input image
protected void	processFeatureCandidate(int x, int y, float value, boolean maximum) Examines a local spatial extremum and interpolates its coordinates using a quadratic function.

Methods inherited from class java.lang.Object

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

Field Detail

scaleSpace

protected SiftScaleSpace scaleSpace

pixelScaleToInput

protected double pixelScaleToInput

detections

protected org.ddogleg.struct.FastQueue<ScalePoint> detections

Constructor Detail

SiftDetector

public SiftDetector(SiftScaleSpace scaleSpace,
                    double edgeR,
                    NonMaxLimiter extractor)

Configures SIFT detector

Parameters:

scaleSpace - Provides the scale space

edgeR - Threshold used to remove edge responses. Larger values means its less strict. Try 10

extractor - Spatial feature detector that can be configured to limit the number of detected features in each scale.

Method Detail

process

public void process(boofcv.struct.image.GrayF32 input)

Detects SIFT features inside the input image

Parameters:

input - Input image. Not modified.

detectFeatures

protected void detectFeatures(int scaleIndex)

Detect features inside the Difference-of-Gaussian image at the current scale

Parameters:

scaleIndex - Which scale in the octave is it detecting features inside up. Primarily provided here for use in child classes.

processFeatureCandidate

protected void processFeatureCandidate(int x,
                                       int y,
                                       float value,
                                       boolean maximum)

Examines a local spatial extremum and interpolates its coordinates using a quadratic function. Very first thing it does is check to see if the feature is really an edge/false positive. After that interpolates the coordinate independently using a quadratic function along each axis. Resulting coordinate will be in the image image's coordinate system.

Parameters:

x - x-coordinate of extremum

y - y-coordinate of extremum

value - value of the extremum

maximum - true if it was a maximum

handleDetection

protected void handleDetection(ScalePoint p)

Function for handling a detected point. Does nothing here, but can be used by a child class to process detections

Parameters:

p - Detected point in scale-space.

getDetections

public org.ddogleg.struct.FastQueue<ScalePoint> getDetections()