PRTools manual in preparation -- Representation preprocessing

The below routines are useful for preprocessing raw data into a set of multidimensional images or one-dimensional signals. They may help to give objects get the same size and comparable orientations, positions and intensities. This is needed to build a proper representation by which objects can be reliably related by features, pixels or dissimilarities.

> Representation Preprocessing
General routines
`filtm`
`filtim`
`dipim`*
Binary image processing
`im_bdilation`*
`im_berosion`*
`im_bpropag`
`im_center`
`im_resize`
`im_invert`
`im_label`*
Grey value image processing
`im_maxf`*
`im_minf`*
`im_fft`
`im_gaussf`
`im_gauss`
`im_gray`
`im_hist_e`
`im_invert`
`im_maxf`
`im_minf`
`im_norm`
`im_skel`
`im_skel_meas`
`im_stretch`
`im_threshold`
`im_unif`

General mapping for applying user defined routines to all objects in the dataset or datafile General mapping for applying user defined routines to all images in the dataset or datafile run any DIPimage command with one input image Binary dilation of images stored in a dataset Binary erosion of images stored in a dataset ation* Binary propagation of images stored in a dataset Center objects in a binary image Resize of object images in datasets and datafiles Invert image by subtraction from its maximum Labeling binary images Maximum filter Minimum filter FFT transform (and more) Gaussian filtering by DipImage Gaussian filtering by Matlab Multi-band to gray-value conversion qualize Histogram equalization Invert image Maximum filter Minimum filter Normalize images w.r.t. mean and variance Skeleton of binary image Skeleton measurements Contrast stretching of images Threshold images Uniform filtering