edgecluster               package:edci               R Documentation

_E_d_g_e _d_e_t_e_c_t_i_o_n _i_n _n_o_i_s_y _i_m_a_g_e_s

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

     'edgecluster' is a simple combination of 'edgepoints' and
     'oregMclust'. It just passes the results of 'edgepoints' to
     'oregMclust'.

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

       edgecluster(data, h1n, h2n,
                   maxval,
                   bw         = max(h1n,h2n)/qnorm(0.975),
                   asteps     = 4,
                   estimator  = "M_median",
                   kernel     = "gauss",
                   score      = "gauss",
                   sigma      = 1,
                   kernelfunc = NULL)

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

    data: See description of 'edgepoints'.

h1n, h2n: See description of 'edgepoints'.

  asteps: See description of 'edgepoints'.

estimator: See description of 'edgepoints'.

  kernel: See description of 'edgepoints'.

   score: See description of 'edgepoints'.

   sigma: See description of 'edgepoints'.

kernelfunc: See description of 'edgepoints'.

  maxval: See description of 'eplist'.

      bw: See description of 'oregMclust'. See default gives the
          observations within the ''edgecluster'-window' 95% of the
          weight.

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

     Return value is a list of two numerical matrices. The first matrix
     contains the results of 'oregMclust', which are the
     'edgeclusters'. The second matrix contains the result of
     'edgepoints'.

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

     Tim Garlipp, garlipp@mathematik.uni-oldenburg.de

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

     'edgepoints', 'oregMclust', 'eplist'

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

       y <- matrix(rep(0,60*60),nrow=60)
       y[21:40,21:40]<-1
       y <- y + matrix(rnorm(60*60,0,0.2),nrow=60)
       image(y,col=gray(seq(0,1,1/255)))

       ec <- edgecluster(y,0.05,0.05,0.7,
                         estimator="M_median",kernel="gauss")
       plot(bestMclust(ec[[1]],4),ec[[2]],xlim=c(0,1),ylim=c(0,1))

