public class FactoryIntensityPointAlg
extends java.lang.Object
FactoryIntensityPoint| Constructor and Description |
|---|
FactoryIntensityPointAlg() |
| Modifier and Type | Method and Description |
|---|---|
static <T extends boofcv.struct.image.ImageGray> |
fast(int pixelTol,
int minCont,
java.lang.Class<T> imageType)
Common interface for creating a
FastCornerIntensity from different image types. |
static <D extends boofcv.struct.image.ImageGray> |
harris(int windowRadius,
float kappa,
boolean weighted,
java.lang.Class<D> derivType)
Common interface for creating a
HarrisCornerIntensity from different
image types. |
static <D extends boofcv.struct.image.ImageGray> |
shiTomasi(int windowRadius,
boolean weighted,
java.lang.Class<D> derivType)
Common interface for creating a
ShiTomasiCornerIntensity from
different image types. |
public static <T extends boofcv.struct.image.ImageGray> FastCornerIntensity<T> fast(int pixelTol, int minCont, java.lang.Class<T> imageType)
FastCornerIntensity from different image types.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.public static <D extends boofcv.struct.image.ImageGray> HarrisCornerIntensity<D> harris(int windowRadius, float kappa, boolean weighted, java.lang.Class<D> derivType)
HarrisCornerIntensity from different
image types.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 cornerpublic static <D extends boofcv.struct.image.ImageGray> ShiTomasiCornerIntensity<D> shiTomasi(int windowRadius, boolean weighted, java.lang.Class<D> derivType)
ShiTomasiCornerIntensity from
different image types.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.