Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 2 docx
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 10 potx
... projections is 1 deg.
%
I = zeros( 128 , 128 ); % Generate image
I (22 :54 ,22 : 52) = .25 ; % Four squares of different shades
I(76;106 ,22 : 52) = .5; % against a black background
I (22 : 52, 76:106) = .75;
I(76:106,76:106) ... texture image is constructed by
taking the regional standard deviation (Figure 12. 14). After thresholding, this
texture-based image is also combined with the...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 9 pot
... blood cell images in Figure 12. 2 with (left) and without (right) boundaries pixel
masked. Fewer inappropriate pixels are seen in the right image.
Example 12. 1, and Figure 12. 2 shows images and associated ... image and the original image. )
9. Load a frame of the MRI image (
mri.tif
) and perform a spatial transfor-
mation that first expands the image horizontally by 20...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 8 pptx
... Summary of Image Classes, Data Formats,
and Conversion Routines
Class Formats supported Conversion routines
Indexed All gray2ind
1
, grayslice
1
, rgb2ind
1
Intensity All ind2gray
2
, mat2gray
2, 3
, ... implemented using the routine
‘conv2’
:
I2 = conv2(I1, h, shape)
where
I1
and
h
are image and filter coefficients (or two images, or simply two
matrices) to be convolved and
sh...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 7 potx
... proportions
D(1,:) = .5*y ؉ .5*x ؉ .1*rand(1,N);
D (2, :) = .2* y ؉ .7*x ؉ .1*rand(1,N);
D(3,:) = .7*y ؉ .2* x ؉ .1*rand(1,N);
D(4,:) = 6*y ؉ 24 *x ؉ .2* rand(1,N);
D(5,:) = .6* rand(1,N); % Noise only
%
% Center ... indicate
that they are actually column vectors.
24 3
TLFeBOOK
PCA and ICA 26 5
and weaknesses, can be found in Hyva
¨
rinen et al. (20 01), as well as Cichicki et
al. (20...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 6 pptx
... = abs(fft(h1 ,25 6)); % Calculate and plot filter
LPF = abs(fft(h0 ,25 6)); % transfer fun of high- and
% lowpass filters
freq = (1: 128 )* 1000 /25 6; % Assume fs = 1000 Hz
subplot(L؉1 ,2, 2*i؉1);
plot(freq, ... below.
Φ(ω) = H
0
ͩ
ω
2
ͪͫ
H
0
ͩ
ω
4
ͪ
Φ
ͩ
ω
4
ͪ
ͬ
(28 )
= H
0
ͩ
ω
2
ͪ
H
0
ͩ
ω
4
ͪ
H
0
ͩ
ω
8
ͪ
H
0
ͩ
ω
2
N
ͪ
Φ
ͩ
ω
2
N
ͪ
(29 )
In the limit as N →∞, Eq. (29 ) becomes:...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 5 pptx
... =
∫
∞
−∞
e
−j2 πf τ
x(t −
τ
2
)x(t −
τ
2
)dτ (9a)
W(n,m) = 2
∑
∞
k=−∞
e
2 nm/N
x(n + k)x*(n − k)(9b)
W(n,m) =
∑
∞
m=−∞
e
2 nm/N
R
x
(n,k) = FFT
k
[R
x
(n,k)] (9c)
Note that t = nT
s
,andf = m/(NT
s
)
The ... (17)
G(1,1) = 1; % Compute one quadrant then expand
for j = 2: N /2
wt = 0;
for i = 1:N /2
G(i,j) = exp(-(sigma*(i-1)v2)/(4*(j-1)v2));
wt = wt ؉ 2* G(i,j);
end
TLFeBOOK
... r...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 4 ppt
... code.)
b(n) =
sin [2 f
L
T(n − L /2) ]
π(n − L /2)
+
sin[π(n − L /2) ]
π(n − L /2)
−
sin [2 f
H
T
s
(n − L /2) ]
π(n − L /2)
Bandstop (13)
An FIR bandpass filter designed using Eq. ( 12) is shown in Figure ... 4.5 and Figure 4.13
% Bandstop filter with a passband gain of 1 between 0 and 100,
% a stopband gain of -40 db between 300 and 400 Hz,
% and an upper passband gain of 1 betwe...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 3 pot
... commands and spectral analysis commands in a loop and calculate
the spectrum five times plotting the five spectra superimposed. Repeat using the
Welch method and data segment length of 128 and ... and can be found in the help file for
pwelch
.
Example 3 .2 Apply Welch’s method to the sine plus noise data used in
Example 3.1. Use 124 -point data segments and a 50% overlap.
% Examp...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 2 docx
... 4.
Example 2. 2 Generate bandlimited noise and compute and plot the auto-
correlation function for two different bandwidths.
% Example 2. 2 and Figure 2. 8
% Generate colored noise having two different bandwidths
% ... quantization step size, δ=V
MAX
/2
N
. Assuming
that the error is uniformly distributed between −δ /2 +δ /2, the variance, σ,is:
σ=
∫
δ /2
−δ /2
η
2
/δ dη=V
2...
Biosignal and Biomedical Image Processing MATLAB-Based Applications Muya phần 1 pptx
... Coding Schemes 27 3
Data Formats 27 5
Data Conversions 27 5
Image Display 27 8
Image Storage and Retrieval 28 4
Basic Arithmetic Operations 28 5
Advanced Protocols: Block Processing 29 2
Sliding Neighborhood ... Wiener Filters 21 3
MATLAB Implementation 21 6
Adaptive Signal Processing 22 2
Adaptive Noise Cancellation 22 6
MATLAB Implementation 22 7
Phase Sensitive Detect...
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