Y = RESIZE(X,NEWSIZE) resizes input array X using a DCT (discrete cosine transform) method. X can be any array of any size. Output Y is of size NEWSIZE.
Input and output formats: Y has the same class as X.
As an example, if you want to multiply the size of an RGB image by a factor N, use the following syntax: newsize = size(I).*[N N 1]; J = resize(I,newsize);
------ % Upsample and stretch an RGB...
IDX = OTSU(I,N) segments the image I into N classes by means of Otsu's N-thresholding method. OTSU returns an array IDX containing the cluster indices (from 1 to N) of each point. IDX = OTSU(I) uses two classes (N=2, default value).
LABEL is a generalization of BWLABEL: BWLABEL works with 2-D binary images only, whereas LABEL works with 2-D arrays of any class. L = LABEL(I,N) returns a matrix L, of the same size as I, containing labels for the connected components...
Y = DCTN(X) returns the discrete cosine transform (DCT) of X.
X = IDCTN(Y) returns the inverse discrete cosine transform (IDCT) of Y.
Y = DSTN(X) returns the discrete sine transform (DST) of X.
X = IDSTN(Y) returns...
Suppose that you have a signal Y (Y can be a time series, a parametric surface or a volumetric data series) corrupted by a Gaussian noise with unknown variance. It is often of interest to know more about this variance. EVAR(Y) thus returns an...
Y = INPAINTN(X) replaces the missing data in X by extra/interpolating the non-missing elements. The non finite values (NaN or Inf) in X are considered as missing data. X can be any N-D array. Important note: ----------------
[Vx2,Vy2] = PPPIV(Vx1,Vy1) carries out robust post-processing of 2-D PIV velocity data. Vx1 and Vy1 must be two matrices of same size that contain the x- and y-components of the velocities at equally spaced points in the Cartesian plane.
LABEL is a generalization of BWLABEL: BWLABEL works with 2-D binary images only, whereas LABEL works with 2-D arrays of any class. L = LABEL(I,N) returns a matrix L, of the same size as I, containing labels for the connected components...
SMOOTHN provides a fast, unsupervised and robust discretized spline smoother for data of any dimension.
SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y can be any N-D noisy array (time series, images, 3D data,...).
B = HMF(A,N) performs hybrid median filtering of the matrix A using a NxN box. Hybrid median filtering preserves edges better than a NxN square kernel-based median filter because data from different spatial directions are ranked separately. Three... |