Question

In: Electrical Engineering

Curvelet feature using fdct wrapping. Calculate the disp_coef also if you know then only try. image...

Curvelet feature using fdct wrapping.

Calculate the disp_coef also

if you know then only try.

image processing electrical

Solutions

Expert Solution

function [f] = all_band_feature_vector_curvelet( iname )

a=iname;
%disp('Take curvelet transform: fdct_wrapping');
C = fdct_wrapping(a,0);
m=size(C,2); % no of level
f=[];
%global k;
for i=1:m
n=size(C{1,i},2); % no of subbands in each level
n1=ceil(n/2); % Symmetric subbands are discarded
for j=1:n1
[x,y]=size(C{i}{j});
coeff=reshape((C{i}{j})',1,x*y);
%sort_coeff=sort(coeff,'descend');
%k=input('Enter the number of largest coefficients U want to take: ');
%largest_coeff=sort_coeff(1:k);
if isempty(f)
f=coeff;
else
f=[f, coeff];
end
end
end
f;
size(f);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function C = fdct_wrapping(x, is_real, finest, nbscales, nbangles_coarse)

% fdct_wrapping.m - Fast Discrete Curvelet Transform via wedge wrapping - Version 1.0
%
% Inputs
% x M-by-N matrix
%
% Optional Inputs
% is_real Type of the transform
% 0: complex-valued curvelets
% 1: real-valued curvelets
% [default set to 0]
% finest Chooses one of two possibilities for the coefficients at the
% finest level:
% 1: curvelets
% 2: wavelets
% [default set to 2]
% nbscales number of scales including the coarsest wavelet level
% [default set to ceil(log2(min(M,N)) - 3)]
% nbangles_coarse
% number of angles at the 2nd coarsest level, minimum 8,
% must be a multiple of 4. [default set to 16]
%
% Outputs
% C Cell array of curvelet coefficients.
% C{j}{l}(k1,k2) is the coefficient at
% - scale j: integer, from finest to coarsest scale,
% - angle l: integer, starts at the top-left corner and
% increases clockwise,
% - position k1,k2: both integers, size varies with j
% and l.
% If is_real is 1, there are two types of curvelets,
% 'cosine' and 'sine'. For a given scale j, the 'cosine'
% coefficients are stored in the first two quadrants (low
% values of l), the 'sine' coefficients in the last two
% quadrants (high values of l).  
%
% See also ifdct_wrapping.m, fdct_wrapping_param.m
%
% By Laurent Demanet, 2004

X = fftshift(fft2(ifftshift(x)))/sqrt(prod(size(x)));
[N1,N2] = size(X);
if nargin < 2, is_real = 0; end;
if nargin < 3, finest = 2; end;
if nargin < 4, nbscales = ceil(log2(min(N1,N2)) - 3); end;
if nargin < 5, nbangles_coarse = 16; end;

% Initialization: data structure
nbangles = [1, nbangles_coarse .* 2.^(ceil((nbscales-(nbscales:-1:2))/2))];
if finest == 2, nbangles(nbscales) = 1; end;
C = cell(1,nbscales);
for j = 1:nbscales
C{j} = cell(1,nbangles(j));
end;

% Loop: pyramidal scale decomposition
M1 = N1/3;
M2 = N2/3;
if finest == 1,

% Initialization: smooth periodic extension of high frequencies
bigN1 = 2*floor(2*M1)+1;
bigN2 = 2*floor(2*M2)+1;
equiv_index_1 = 1+mod(floor(N1/2)-floor(2*M1)+(1:bigN1)-1,N1);
equiv_index_2 = 1+mod(floor(N2/2)-floor(2*M2)+(1:bigN2)-1,N2);
X = X(equiv_index_1,equiv_index_2);
% Invariant: equiv_index_1(floor(2*M1)+1) == (N1 + 2 - mod(N1,2))/2
% is the center in frequency. Same for M2, N2.
window_length_1 = floor(2*M1) - floor(M1) - 1 - (mod(N1,3)==0);
window_length_2 = floor(2*M2) - floor(M2) - 1 - (mod(N2,3)==0);
% Invariant: floor(M1) + floor(2*M1) == N1 - (mod(M1,3)~=0)
% Same for M2, N2.
coord_1 = 0:(1/window_length_1):1;
coord_2 = 0:(1/window_length_2):1;
[wl_1,wr_1] = fdct_wrapping_window(coord_1);
[wl_2,wr_2] = fdct_wrapping_window(coord_2);
lowpass_1 = [wl_1, ones(1,2*floor(M1)+1), wr_1];
if mod(N1,3)==0, lowpass_1 = [0, lowpass_1, 0]; end;
lowpass_2 = [wl_2, ones(1,2*floor(M2)+1), wr_2];
if mod(N2,3)==0, lowpass_2 = [0, lowpass_2, 0]; end;
lowpass = lowpass_1'*lowpass_2;
Xlow = X .* lowpass;

scales = nbscales:-1:2;

else
  
M1 = M1/2;
M2 = M2/2;
window_length_1 = floor(2*M1) - floor(M1) - 1;
window_length_2 = floor(2*M2) - floor(M2) - 1;
coord_1 = 0:(1/window_length_1):1;
coord_2 = 0:(1/window_length_2):1;
[wl_1,wr_1] = fdct_wrapping_window(coord_1);
[wl_2,wr_2] = fdct_wrapping_window(coord_2);
lowpass_1 = [wl_1, ones(1,2*floor(M1)+1), wr_1];
lowpass_2 = [wl_2, ones(1,2*floor(M2)+1), wr_2];
lowpass = lowpass_1'*lowpass_2;
hipass = sqrt(1 - lowpass.^2);
Xlow_index_1 = ((-floor(2*M1)):floor(2*M1)) + ceil((N1+1)/2);
Xlow_index_2 = ((-floor(2*M2)):floor(2*M2)) + ceil((N2+1)/2);
Xlow = X(Xlow_index_1, Xlow_index_2) .* lowpass;
Xhi = X;
Xhi(Xlow_index_1, Xlow_index_2) = Xhi(Xlow_index_1, Xlow_index_2) .* hipass;
C{nbscales}{1} = fftshift(ifft2(ifftshift(Xhi)))*sqrt(prod(size(Xhi)));
if is_real, C{nbscales}{1} = real(C{nbscales}{1}); end;
  
scales = (nbscales-1):-1:2;

end;
for j = scales,

M1 = M1/2;
M2 = M2/2;
window_length_1 = floor(2*M1) - floor(M1) - 1;
window_length_2 = floor(2*M2) - floor(M2) - 1;
coord_1 = 0:(1/window_length_1):1;
coord_2 = 0:(1/window_length_2):1;
[wl_1,wr_1] = fdct_wrapping_window(coord_1);
[wl_2,wr_2] = fdct_wrapping_window(coord_2);
lowpass_1 = [wl_1, ones(1,2*floor(M1)+1), wr_1];
lowpass_2 = [wl_2, ones(1,2*floor(M2)+1), wr_2];
lowpass = lowpass_1'*lowpass_2;
hipass = sqrt(1 - lowpass.^2);
Xhi = Xlow; % size is 2*floor(4*M1)+1 - by - 2*floor(4*M2)+1
Xlow_index_1 = ((-floor(2*M1)):floor(2*M1)) + floor(4*M1) + 1;
Xlow_index_2 = ((-floor(2*M2)):floor(2*M2)) + floor(4*M2) + 1;
Xlow = Xlow(Xlow_index_1, Xlow_index_2);
Xhi(Xlow_index_1, Xlow_index_2) = Xlow .* hipass;
Xlow = Xlow .* lowpass; % size is 2*floor(2*M1)+1 - by - 2*floor(2*M2)+1
  
% Loop: angular decomposition
l = 0;
nbquadrants = 2 + 2*(~is_real);
nbangles_perquad = nbangles(j)/4;
for quadrant = 1:nbquadrants
M_horiz = M2 * (mod(quadrant,2)==1) + M1 * (mod(quadrant,2)==0);
M_vert = M1 * (mod(quadrant,2)==1) + M2 * (mod(quadrant,2)==0);
if mod(nbangles_perquad,2),
wedge_ticks_left = round((0:(1/(2*nbangles_perquad)):.5)*2*floor(4*M_horiz) + 1);
wedge_ticks_right = 2*floor(4*M_horiz) + 2 - wedge_ticks_left;
wedge_ticks = [wedge_ticks_left, wedge_ticks_right(end:-1:1)];
else
wedge_ticks_left = round((0:(1/(2*nbangles_perquad)):.5)*2*floor(4*M_horiz) + 1);
wedge_ticks_right = 2*floor(4*M_horiz) + 2 - wedge_ticks_left;
wedge_ticks = [wedge_ticks_left, wedge_ticks_right((end-1):-1:1)];
end;
wedge_endpoints = wedge_ticks(2:2:(end-1)); % integers
wedge_midpoints = (wedge_endpoints(1:(end-1)) + wedge_endpoints(2:end))/2;
% integers or half-integers
  
% Left corner wedge
l = l+1;
first_wedge_endpoint_vert = round(2*floor(4*M_vert)/(2*nbangles_perquad) + 1);
length_corner_wedge = floor(4*M_vert) - floor(M_vert) + ceil(first_wedge_endpoint_vert/4);
Y_corner = 1:length_corner_wedge;
[XX,YY] = meshgrid(1:(2*floor(4*M_horiz)+1),Y_corner);
width_wedge = wedge_endpoints(2) + wedge_endpoints(1) - 1;
slope_wedge = (floor(4*M_horiz) + 1 - wedge_endpoints(1))/floor(4*M_vert);
left_line = round(2 - wedge_endpoints(1) + slope_wedge*(Y_corner - 1));
% integers
[wrapped_data, wrapped_XX, wrapped_YY] = deal(zeros(length_corner_wedge,width_wedge));
first_row = floor(4*M_vert)+2-ceil((length_corner_wedge+1)/2)+...
mod(length_corner_wedge+1,2)*(quadrant-2 == mod(quadrant-2,2));
first_col = floor(4*M_horiz)+2-ceil((width_wedge+1)/2)+...
mod(width_wedge+1,2)*(quadrant-3 == mod(quadrant-3,2));
% Coordinates of the top-left corner of the wedge wrapped
% around the origin. Some subtleties when the wedge is
% even-sized because of the forthcoming 90 degrees rotation
for row = Y_corner
cols = left_line(row) + mod((0:(width_wedge-1))-(left_line(row)-first_col),width_wedge);
admissible_cols = round(1/2*(cols+1+abs(cols-1)));
new_row = 1 + mod(row - first_row, length_corner_wedge);
wrapped_data(new_row,:) = Xhi(row,admissible_cols) .* (cols > 0);
wrapped_XX(new_row,:) = XX(row,admissible_cols);
wrapped_YY(new_row,:) = YY(row,admissible_cols);
end;
slope_wedge_right = (floor(4*M_horiz)+1 - wedge_midpoints(1))/floor(4*M_vert);
mid_line_right = wedge_midpoints(1) + slope_wedge_right*(wrapped_YY - 1);
% not integers in general
coord_right = 1/2 + floor(4*M_vert)/(wedge_endpoints(2) - wedge_endpoints(1)) * ...
(wrapped_XX - mid_line_right)./(floor(4*M_vert)+1 - wrapped_YY);
C2 = 1/(1/(2*(floor(4*M_horiz))/(wedge_endpoints(1) - 1) - 1) + 1/(2*(floor(4*M_vert))/(first_wedge_endpoint_vert - 1) - 1));
C1 = C2 / (2*(floor(4*M_vert))/(first_wedge_endpoint_vert - 1) - 1);
wrapped_XX((wrapped_XX - 1)/floor(4*M_horiz) + (wrapped_YY-1)/floor(4*M_vert) == 2) = ...
wrapped_XX((wrapped_XX - 1)/floor(4*M_horiz) + (wrapped_YY-1)/floor(4*M_vert) == 2) + 1;
coord_corner = C1 + C2 * ((wrapped_XX - 1)/(floor(4*M_horiz)) - (wrapped_YY - 1)/(floor(4*M_vert))) ./ ...
(2-((wrapped_XX - 1)/(floor(4*M_horiz)) + (wrapped_YY - 1)/(floor(4*M_vert))));
wl_left = fdct_wrapping_window(coord_corner);
[wl_right,wr_right] = fdct_wrapping_window(coord_right);
wrapped_data = wrapped_data .* (wl_left .* wr_right);

switch is_real
case 0
wrapped_data = rot90(wrapped_data,-(quadrant-1));
C{j}{l} = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
case 1
wrapped_data = rot90(wrapped_data,-(quadrant-1));
x = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
C{j}{l} = sqrt(2)*real(x);
C{j}{l+nbangles(j)/2} = sqrt(2)*imag(x);
end;
  
% Regular wedges
length_wedge = floor(4*M_vert) - floor(M_vert);
Y = 1:length_wedge;
first_row = floor(4*M_vert)+2-ceil((length_wedge+1)/2)+...
mod(length_wedge+1,2)*(quadrant-2 == mod(quadrant-2,2));
for subl = 2:(nbangles_perquad-1);
l = l+1;
width_wedge = wedge_endpoints(subl+1) - wedge_endpoints(subl-1) + 1;
slope_wedge = ((floor(4*M_horiz)+1) - wedge_endpoints(subl))/floor(4*M_vert);
left_line = round(wedge_endpoints(subl-1) + slope_wedge*(Y - 1));
[wrapped_data, wrapped_XX, wrapped_YY] = deal(zeros(length_wedge,width_wedge));
first_col = floor(4*M_horiz)+2-ceil((width_wedge+1)/2)+...
mod(width_wedge+1,2)*(quadrant-3 == mod(quadrant-3,2));
for row = Y
cols = left_line(row) + mod((0:(width_wedge-1))-(left_line(row)-first_col),width_wedge);
new_row = 1 + mod(row - first_row, length_wedge);
wrapped_data(new_row,:) = Xhi(row,cols);
wrapped_XX(new_row,:) = XX(row,cols);
wrapped_YY(new_row,:) = YY(row,cols);
end;
slope_wedge_left = ((floor(4*M_horiz)+1) - wedge_midpoints(subl-1))/floor(4*M_vert);
mid_line_left = wedge_midpoints(subl-1) + slope_wedge_left*(wrapped_YY - 1);
coord_left = 1/2 + floor(4*M_vert)/(wedge_endpoints(subl) - wedge_endpoints(subl-1)) * ...
(wrapped_XX - mid_line_left)./(floor(4*M_vert)+1 - wrapped_YY);
slope_wedge_right = ((floor(4*M_horiz)+1) - wedge_midpoints(subl))/floor(4*M_vert);
mid_line_right = wedge_midpoints(subl) + slope_wedge_right*(wrapped_YY - 1);
coord_right = 1/2 + floor(4*M_vert)/(wedge_endpoints(subl+1) - wedge_endpoints(subl)) * ...
(wrapped_XX - mid_line_right)./(floor(4*M_vert)+1 - wrapped_YY);
wl_left = fdct_wrapping_window(coord_left);
[wl_right,wr_right] = fdct_wrapping_window(coord_right);
wrapped_data = wrapped_data .* (wl_left .* wr_right);
switch is_real
case 0
wrapped_data = rot90(wrapped_data,-(quadrant-1));
C{j}{l} = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
case 1
wrapped_data = rot90(wrapped_data,-(quadrant-1));
x = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
C{j}{l} = sqrt(2)*real(x);
C{j}{l+nbangles(j)/2} = sqrt(2)*imag(x);
end;
end;

% Right corner wedge
l = l+1;
width_wedge = 4*floor(4*M_horiz) + 3 - wedge_endpoints(end) - wedge_endpoints(end-1);
slope_wedge = ((floor(4*M_horiz)+1) - wedge_endpoints(end))/floor(4*M_vert);
left_line = round(wedge_endpoints(end-1) + slope_wedge*(Y_corner - 1));
[wrapped_data, wrapped_XX, wrapped_YY] = deal(zeros(length_corner_wedge,width_wedge));
first_row = floor(4*M_vert)+2-ceil((length_corner_wedge+1)/2)+...
mod(length_corner_wedge+1,2)*(quadrant-2 == mod(quadrant-2,2));
first_col = floor(4*M_horiz)+2-ceil((width_wedge+1)/2)+...
mod(width_wedge+1,2)*(quadrant-3 == mod(quadrant-3,2));
for row = Y_corner
cols = left_line(row) + mod((0:(width_wedge-1))-(left_line(row)-first_col),width_wedge);
admissible_cols = round(1/2*(cols+2*floor(4*M_horiz)+1-abs(cols-(2*floor(4*M_horiz)+1))));
new_row = 1 + mod(row - first_row, length_corner_wedge);
wrapped_data(new_row,:) = Xhi(row,admissible_cols) .* (cols <= (2*floor(4*M_horiz)+1));
wrapped_XX(new_row,:) = XX(row,admissible_cols);
wrapped_YY(new_row,:) = YY(row,admissible_cols);
end;
slope_wedge_left = ((floor(4*M_horiz)+1) - wedge_midpoints(end))/floor(4*M_vert);
mid_line_left = wedge_midpoints(end) + slope_wedge_left*(wrapped_YY - 1);
coord_left = 1/2 + floor(4*M_vert)/(wedge_endpoints(end) - wedge_endpoints(end-1)) * ...
(wrapped_XX - mid_line_left)./(floor(4*M_vert) + 1 - wrapped_YY);
C2 = -1/(2*(floor(4*M_horiz))/(wedge_endpoints(end) - 1) - 1 + 1/(2*(floor(4*M_vert))/(first_wedge_endpoint_vert - 1) - 1));
C1 = -C2 * (2*(floor(4*M_horiz))/(wedge_endpoints(end) - 1) - 1);
wrapped_XX((wrapped_XX - 1)/floor(4*M_horiz) == (wrapped_YY - 1)/floor(4*M_vert)) = ...
wrapped_XX((wrapped_XX - 1)/floor(4*M_horiz) == (wrapped_YY - 1)/floor(4*M_vert)) - 1;
coord_corner = C1 + C2 * (2-((wrapped_XX - 1)/(floor(4*M_horiz)) + (wrapped_YY - 1)/(floor(4*M_vert)))) ./ ...
((wrapped_XX - 1)/(floor(4*M_horiz)) - (wrapped_YY - 1)/(floor(4*M_vert)));
wl_left = fdct_wrapping_window(coord_left);
[wl_right,wr_right] = fdct_wrapping_window(coord_corner);

wrapped_data = wrapped_data .* (wl_left .* wr_right);
switch is_real
case 0
wrapped_data = rot90(wrapped_data,-(quadrant-1));
C{j}{l} = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
case 1
wrapped_data = rot90(wrapped_data,-(quadrant-1));
x = fftshift(ifft2(ifftshift(wrapped_data)))*sqrt(prod(size(wrapped_data)));
C{j}{l} = sqrt(2)*real(x);
C{j}{l+nbangles(j)/2} = sqrt(2)*imag(x);
end;

if quadrant < nbquadrants, Xhi = rot90(Xhi); end;
end;
end;

% Coarsest wavelet level
C{1}{1} = fftshift(ifft2(ifftshift(Xlow)))*sqrt(prod(size(Xlow)));
if is_real == 1,
C{1}{1} = real(C{1}{1});
end;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc;
clear all;
close all;
a=imread('032.bmp');
%a=imread('54.png');
imshow(a)
%% C= Curvelet coefficients
C = fdct_wrapping(a,0);
B=C{1,1}{1,1};
%B=C{1}{1};
%% img= Image containing all the curvelet coefficients. The coefficents are rescaled so that the largest coefficent in each subband has unit norm.
img = fdct_wrapping_dispcoef(C);
figure;imshow(img)
figure;
colormap gray; imagesc(real(B)); ...
title('a curvelet: approximation sub-band');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function img = fdct_wrapping_dispcoef(C)

% fdct_wrapping_dispcoef - returns an image containing all the curvelet coefficients
%
% Inputs
% C Curvelet coefficients
%
% Outputs
% img Image containing all the curvelet coefficients. The coefficents are rescaled so that
% the largest coefficent in each subband has unit norm.
%
  
[m,n] = size(C{end}{1});
nbscales = floor(log2(min(m,n)))-3;
  
img = C{1}{1}; img = img/max(max(abs(img))); %normalize
for sc=2:nbscales-1
nd = length(C{sc})/4;
wcnt = 0;
  
ONE = [];
[u,v] = size(C{sc}{wcnt+1});
for w=1:nd
ONE = [ONE, fdct_wrapping_dispcoef_expand(u,v,C{sc}{wcnt+w})];
end
wcnt = wcnt+nd;
  
TWO = [];
[u,v] = size(C{sc}{wcnt+1});
for w=1:nd
TWO = [TWO; fdct_wrapping_dispcoef_expand(u,v,C{sc}{wcnt+w})];
end
wcnt = wcnt+nd;
  
THREE = [];
[u,v] = size(C{sc}{wcnt+1});
for w=1:nd
THREE = [fdct_wrapping_dispcoef_expand(u,v,C{sc}{wcnt+w}), THREE];
end
wcnt = wcnt+nd;
  
FOUR = [];
[u,v] = size(C{sc}{wcnt+1});
for w=1:nd
FOUR = [fdct_wrapping_dispcoef_expand(u,v,C{sc}{wcnt+w}); FOUR];
end
wcnt = wcnt+nd;
  
[p,q] = size(img);
[a,b] = size(ONE);
[g,h] = size(TWO);
m = 2*a+g; n = 2*h+b; %size of new image
scale = max(max( max(max(abs(ONE))),max(max(abs(TWO))) ), max(max(max(abs(THREE))), max(max(abs(FOUR))) )); %scaling factor
  
new = 0.5 * ones(m,n);%background value
new(a+1:a+g,1:h) = FOUR/scale;
new(a+g+1:2*a+g,h+1:h+b) = THREE/scale;
new(a+1:a+g,h+b+1:2*h+b) = TWO/scale;
new(1:a,h+1:h+b) = ONE/scale;%normalize
  
dx = floor((g-p)/2); dy = floor((b-q)/2);
  
new(a+1+dx:a+p+dx,h+1+dy:h+q+dy) = img;
  
img = new;
end

function A = fdct_wrapping_dispcoef_expand(u,v,B)
A = zeros(u,v);
[p,q] = size(B);
A(1:p,1:q) = B;
  
  
  


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Using survey data to calculate statistics can be extremely valuable, but you must also make sure that the sample and questions are unbiased. Design a pair of questions that are related to the same healthcare issue: one that is unbiased and another that would result in a bias in one direction or the other.   Examples: Do you think that the rate of type II diabetes diagnoses will increase over the next 10 years? Given the large increase in childhood obesity...
Using survey data to calculate statistics can be extremely valuable, but you must also make sure...
Using survey data to calculate statistics can be extremely valuable, but you must also make sure that the sample and questions are unbiased. Design a pair of questions that are related to the same healthcare issue: one that is unbiased and another that would result in a bias in one direction or the other. Examples: Do you think that the rate of type II diabetes diagnoses will increase over the next 10 years? Given the large increase in childhood obesity...
Using survey data to calculate statistics can be extremely valuable, but you must also make sure...
Using survey data to calculate statistics can be extremely valuable, but you must also make sure that the sample and questions are unbiased. Design a pair of questions that are related to the same healthcare issue: one that is unbiased and another that would result in a bias in one direction or the other. Examples: Do you think that the rate of type II diabetes diagnoses will increase over the next 10 years? Given the large increase in childhood obesity...
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