distmap               package:EBImage               R Documentation

_D_i_s_t_a_n_c_e _m_a_p _t_r_a_n_s_f_o_r_m _o_f _b_i_n_a_r_y _i_m_a_g_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Distance map transform of binary images.

_U_s_a_g_e:


         distmap(x, tolerance=0.05, minBG=0.05, strict=FALSE, ...)


_A_r_g_u_m_e_n_t_s:


     '_x' An object of 'Image-class' in the  'Grayscale' mode. 

     '_t_o_l_e_r_a_n_c_e' A numeric value in the range '[0,1)'. Image  pixels
          that differ from background (zero) by less than this value
          will be considered as background. This enables image-wide
          single value threshloding if necessary.  

     '_m_i_n_B_G' An estimated minimum fraction of background. If the 
          estimated number of background pixels is smaller than this
          value the  algorithm will terminate to avoid unnecessarily
          lengthy computations. This  is a protection mechanism in case
          a very noise image is supplied without  first  thresholding
          it. 

     '_s_c_t_r_i_c_t' If 'TRUE', single-pixel diagonal distances  are
          calculated exactly (i.e. 'sqrt(2)'), otherwise these are set
          to be 1, same as neighbouring pixels on the vertical and
          horizontal axes. 

     '...' Reserved for future use. 

_V_a_l_u_e:

     An object of 'Image-class' in the 'Grayscale' mode with pixels
     containing 'floor' of distances to the nearest  background. If
     'strict' is set to 'TRUE', single-pixel diagonal  distances are
     calculated exactly (i.e. 'sqrt(2)'), otherwise these  are set to
     be 1, same as neighbouring pixels on the vertical and horizontal
     axes.To preview the results visually, use  'display(
     normalize(result) )'.

_D_e_t_a_i_l_s:

     Given a binary or grayscale image with zero background, a distance
     map  transformed image contains in each pixel a distance to the
     closest background  pixel (zero). This function is used
     extensively in object detection. Distance  map is a usual input
     for 'watershed' and other segmentation  algorithms that are used
     to identify and separate closely positioned objects.

_A_u_t_h_o_r(_s):

     Copyright (c) 2005-2006 Oleg Sklyar : osklyar@ebi.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     _ImageMagick_: <URL: http://www.imagemagick.org>.

_S_e_e _A_l_s_o:

     ' Image-class, watershed, thresh '

_E_x_a_m_p_l_e_s:

       ## Not run: see ?watershed for examples

