Uses of Class
mpi.cbg.fly.FloatArray2D

Packages that use FloatArray2D

mpi.cbg.fly

Uses of FloatArray2D in mpi.cbg.fly

Methods in mpi.cbg.fly that return FloatArray2D

static FloatArray2D	ImageArrayConverter.ArrayToFloatArray2D(int w, int h, int[] pixels)
FloatArray2D	FloatArray2D.clone()
static FloatArray2D	Filter.computeGaussian(FloatArray2D input, float sigma)
static FloatArray2D	Filter.computeGaussianFastMirror(FloatArray2D input, float sigma)
static FloatArray2D	Filter.computeIncreasingGaussianX(FloatArray2D input, float stDevStart, float stDevEnd)
static FloatArray2D	Filter.computeLaPlaceFilter3(FloatArray2D input)
static FloatArray2D	Filter.computeLaPlaceFilter5(FloatArray2D input)
static FloatArray2D	Filter.convolveSeparable(FloatArray2D input, float[] h, float[] v) convolve an image with a horizontal and a vertical kernel simple straightforward, not optimized---replace this with a trusted better version soon
static FloatArray2D	Filter.create_gaussian_kernel_2D_offset(float sigma, float offset_x, float offset_y, boolean normalize)
static FloatArray2D	Filter.createGaussianKernel2D(float sigma, boolean normalize)
static FloatArray2D[]	Filter.createGradients(FloatArray2D array)
static FloatArray2D	Filter.distortSamplingX(FloatArray2D input)
static FloatArray2D	Filter.distortSamplingY(FloatArray2D input)
FloatArray2D[]	FloatArray2DScaleOctave.getD()
FloatArray2D	FloatArray2DScaleOctave.getD(int i)
FloatArray2D[]	FloatArray2DScaleOctave.getL()
FloatArray2D	FloatArray2DScaleOctave.getL(int i)
FloatArray2D[]	FloatArray2DScaleOctave.getL1(int i) get the gradients of the corresponding gaussian image, generates it on demand, if not yet available.
static FloatArray2D	ImageArrayConverter.ImageToFloatArray2D(java.awt.Image ip)

Methods in mpi.cbg.fly with parameters of type FloatArray2D

static FloatArray2D	Filter.computeGaussian(FloatArray2D input, float sigma)
static FloatArray2D	Filter.computeGaussianFastMirror(FloatArray2D input, float sigma)
static FloatArray2D	Filter.computeIncreasingGaussianX(FloatArray2D input, float stDevStart, float stDevEnd)
static FloatArray2D	Filter.computeLaPlaceFilter3(FloatArray2D input)
static FloatArray2D	Filter.computeLaPlaceFilter5(FloatArray2D input)
static FloatArray2D	Filter.convolveSeparable(FloatArray2D input, float[] h, float[] v) convolve an image with a horizontal and a vertical kernel simple straightforward, not optimized---replace this with a trusted better version soon
static FloatArray2D[]	Filter.createGradients(FloatArray2D array)
static FloatArray2D	Filter.distortSamplingX(FloatArray2D input)
static FloatArray2D	Filter.distortSamplingY(FloatArray2D input)
static void	FloatArray2DScaleOctave.downsample(FloatArray2D src, FloatArray2D dst) downsample src by simply using every second pixel into dst For efficiency reasons, the dimensions of dst are not checked, that is, you have to take care, that dst.width == src.width / 2 + src.width % 2 && dst.height == src.height / 2 + src.height % 2 .
static void	Filter.enhance(FloatArray2D src, float scale) in place enhance all values of a FloatArray to fill the given range
static void	ImageArrayConverter.FloatArrayToFloatProcessor(java.awt.Image ip, FloatArray2D pixels)
static java.util.Vector<Feature>	SIFT.getFeatures(FloatArray2D fa)
void	FloatArray2DSIFT.init(FloatArray2D src, int steps, float initial_sigma, int min_size, int max_size) initialize the scale space as a scale pyramid having octave stubs only
static void	FloatArray2DScaleOctave.upsample(FloatArray2D src, FloatArray2D dst) upsample src by linearly interpolating into dst For efficiency reasons, the dimensions of dst are not checked, that is, you have to take care, that src.width == dst.width / 2 + dst.width % 2 && src.height == dst.height / 2 + dst.height % 2 .

Constructors in mpi.cbg.fly with parameters of type FloatArray2D

FloatArray2DScaleOctave(FloatArray2D img, float[] sigma, float[] sigma_diff, float[][] kernel_diff)
Constructor faster initialisation with precomputed gaussian kernels

FloatArray2DScaleOctave(FloatArray2D img, int steps, float initial_sigma)
Constructor