Class FactoryIntensityPointAlg
java.lang.Object
boofcv.factory.feature.detect.intensity.FactoryIntensityPointAlg
Factory for creating various types of interest point intensity algorithms.
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionstatic <T extends ImageGray<T>>
FastCornerDetector<T>Common interface for creating aFastCornerDetector
from different image types.static <D extends ImageGray<D>>
GradientCornerIntensity<D>Common interface for creating aHarrisCornerIntensity
from different image types.static <D extends ImageGray<D>>
GradientCornerIntensity<D>Common interface for creating aShiTomasiCornerIntensity
from different image types.
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Constructor Details
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FactoryIntensityPointAlg
public FactoryIntensityPointAlg()
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Method Details
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fast
public static <T extends ImageGray<T>> FastCornerDetector<T> fast(int pixelTol, int minCont, Class<T> imageType) Common interface for creating aFastCornerDetector
from different image types.- Parameters:
pixelTol
- How different pixels need to be to be considered part of a corner. Image dependent. Try 20 to start.minCont
- Minimum number of continue pixels in a circle for it ot be a corner. Can be 9,10,11 or 12.imageType
- Type of input image it is computed form.- Returns:
- Fast corner
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harris
public static <D extends ImageGray<D>> GradientCornerIntensity<D> harris(int windowRadius, float kappa, boolean weighted, Class<D> derivType) Common interface for creating aHarrisCornerIntensity
from different image types.- Parameters:
windowRadius
- Size of the feature it is detects,Try 2.kappa
- Tuning parameter, typically a small number around 0.04weighted
- Is the gradient weighted using a Gaussian distribution? Weighted is much slower than unweighted.derivType
- Image derivative type it is computed from. @return Harris corner
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shiTomasi
public static <D extends ImageGray<D>> GradientCornerIntensity<D> shiTomasi(int windowRadius, boolean weighted, Class<D> derivType) Common interface for creating aShiTomasiCornerIntensity
from different image types.- Parameters:
windowRadius
- Size of the feature it detects, Try 2.weighted
- Should the it be weighted by a Gaussian kernel? Unweighted is much faster.derivType
- Image derivative type it is computed from.- Returns:
- KLT corner
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