Calculate weights for image pixels based on image gradient
W = gradientweight(I)
W = gradientweight(I,sigma)
W = gradientweight(___,Name,Value)
the pixel weight for each pixel in image
W = gradientweight(
on the gradient magnitude at that pixel, and returns the weight array
The weight of a pixel is inversely related to the gradient values
at the pixel location. Pixels with small gradient magnitude (smooth
regions) have a large weight and pixels with large gradient magnitude
(such as on the edges) have a small weight.
This example segments an image using the Fast Marching Method based on the weights derived from the image gradient.
Read image and display it.
I = imread('coins.png'); imshow(I) title('Original Image')
Compute weights based on image gradient.
sigma = 1.5; W = gradientweight(I, sigma, 'RolloffFactor', 3, 'WeightCutoff', 0.25);
Select a seed location.
R = 70; C = 216; hold on; plot(C, R, 'r.', 'LineWidth', 1.5, 'MarkerSize',15); title('Original Image with Seed Location')
Segment the image using the weight array.
thresh = 0.1; [BW, D] = imsegfmm(W, C, R, thresh); figure, imshow(BW) title('Segmented Image') hold on; plot(C, R, 'r.', 'LineWidth', 1.5, 'MarkerSize',15);
Geodesic distance matrix
D can be thresholded using different thresholds to get different segmentation results.
figure, imshow(D) title('Geodesic Distances') hold on; plot(C, R, 'r.', 'LineWidth', 1.5, 'MarkerSize',15);
I— Input image
Input image, specified as a grayscale image. Must be nonsparse.
sigma— Standard deviation for Derivative of Gaussian
Standard deviation for Derivative of Gaussian, specified as
a positive scalar of class
sigma = 1.0; W = gradientweight(I, sigma)
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
W = gradientweight(I,1.5,'RolloffFactor',3,'WeightCutoff',0.25);
'RolloffFactor'— Output weight roll-off factor
Output weight roll-off factor, specified as the comma-separated
pair consisting of
'RolloffFactor' and a positive
scalar of class
double. Controls how fast weight
values fall as a function of gradient magnitude. When viewed as a
2-D plot, pixel intensity values might vary gradually at the edges
of regions, creating a gentle slope. In your segmented image, you
might want the edge to be more well-defined. Using the roll-off factor,
you control the slope of the weight value curve at points where intensity
values start to change. If you specify a high value, the output weight
values fall off sharply around the edges of smooth regions. If you
specify a low value, the output weight has a more gradual fall-off
around the edges. The suggested range for this parameter is
'WeightCutoff'— Threshold for weight values
Threshold for weight values, specified as the comma-separated
pair consisting of
'WeightCutoff' and a positive
scalar of class
double. If you use this parameter
to set a threshold on weight values, it suppresses any weight values
less than the value you specify, setting these pixels to a small constant
value (1e-3). This parameter can be useful in improving the accuracy
of the output when you use the output weight array
input to Fast Marching Method segmentation function,
W— Weight array
Weight array, returned as a numeric array. The weight array
is the same size as the input image,
I. The weight
array is of class
in which case it is of class
gradientweight uses double-precision
floating point operations for internal computations for all classes
I, except when
I is of class
in which case
gradientweight uses single-precision
floating point operations internally.