Class FeatureLaplacePyramid<T extends ImageGray<T>,D extends ImageGray<D>>

java.lang.Object
boofcv.alg.feature.detect.interest.FeatureLaplacePyramid<T,D>
All Implemented Interfaces:
InterestPointScaleSpacePyramid<T>

public class FeatureLaplacePyramid<T extends ImageGray<T>,D extends ImageGray<D>> extends Object implements InterestPointScaleSpacePyramid<T>

Feature detector across image pyramids that uses the Laplacian to determine strength in scale-space.

COMMENT ON SCALEPOWER: To normalize feature intensity across scales each feature intensity is multiplied by the scale to the power of 'scalePower'. See [1,2] for how to compute 'scalePower'. Inside of the image pyramid sub-sampling of the image causes the image gradient to be a factor of 'scale' larger than it would be without sub-sampling. In some situations this can negate the need to adjust feature intensity further.

[1] Krystian Mikolajczyk and Cordelia Schmid, "Indexing based on scale invariant interest points" ICCV 2001. Proceedings.
[2] Lindeberg, T., "Feature detection with automatic scale selection." IJCV 30(2) (1998) 79 – 116

See Also:
  • Field Details

  • Constructor Details

    • FeatureLaplacePyramid

      public FeatureLaplacePyramid(GeneralFeatureDetector<T,D> detector, ImageFunctionSparse<T> sparseLaplace, AnyImageDerivative<T,D> computeDerivative, double scalePower)
      Create a feature detector.
      Parameters:
      detector - Point feature detector which is used to find candidates in each scale level
      sparseLaplace - Used to compute the Laplacian at each candidates
      computeDerivative - Used to compute image derivatives
      scalePower - Used to normalize features intensity across scale space. For many features this value should be one.
  • Method Details