find image dimensions

ImageND(3)						User Contributed Perl Documentation						ImageND(3)

NAME

       PDL::ImageND - useful image processing routines which work in N-dimensions

DESCRIPTION

       In some cases (though not as many as one would like) it is possible to write general routines that operate on N-dimensional objects.

       An example in this module is a N-Dim convolution algorithm I made up one day - it works but the boundary condtions are a bit funny.

SYNOPSIS

	use PDL::ImageND;

FUNCTIONS

       convolve

	 Signature: (a(m); b(n); int adims(p); int bdims(q); [o]c(m))

       N-dimensional convolution algorithm.

       $new = convolve $a, $kernel

       Convolve an array with a kernel, both of which are N-dimensional.

       Note because of the algorithm used (writing N-dim routines is not easy on the brain!) the boundary conditions are a bit strange. They wrap,
       but up to the NEXT row/column/cube-slice/etc. If this is a problem consider using zero-padding or something.

       ninterpol()

       N-dimensional interpolation routine

	Signature: ninterpol(point(),data(n),[o]value())

	     $value = ninterpol($point, $data);

       "ninterpol" uses "interpol" to find a linearly interpolated value in N dimensions, assuming the data is spread on a uniform grid.  To use
       an arbitrary grid distribution, need to find the grid-space point from the indexing scheme, then call "ninterpol" -- this is far from triv-
       ial (and ill-defined in general).

       rebin

	 Signature: (a(m); [o]b(n); int ns => n)

       N-dimensional rebinning algorithm

       $new = rebin $a, $dim1, $dim2,..;.  $new = rebin $a, $template; $new = rebin $a, $template, {Norm => 1};

       Rebin an N-dimensional array to newly specified dimensions.  Specifying `Norm' keeps the sum constant, otherwise the intensities are kept
       constant.  If more template dimensions are given than for the input pdl, these dimensions are created; if less, the final dimensions are
       maintained as they were.

       So if $a is a 10 x 10 pdl, then "rebin($a,15)" is a 15 x 10 pdl, while "rebin($a,15,16,17)" is a 15 x 16 x 17 pdl (where the values along
       the final dimension are all identical).

       circ_mean_p

       Calculates the circular mean of an n-dim image and returns the projection. Optionally takes the center to be used.

	  $cmean=circ_mean_p($im);
	  $cmean=circ_mean_p($im,{Center => [10,10]});

       circ_mean

       Smooths an image by applying circular mean.  Optionally takes the center to be used.

	  circ_mean($im);
	  circ_mean($im,{Center => [10,10]});

AUTHORS

       Copyright (C) Karl Glazebrook 1997.  All rights reserved. There is no warranty. You are allowed to redistribute this software / documenta-
       tion under certain conditions. For details, see the file COPYING in the PDL distribution. If this file is separated from the PDL distribu-
       tion, the copyright notice should be included in the file.

perl v5.8.0							    2003-01-29								ImageND(3)