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[cv]edge detection: gradients

程序员文章站 2024-01-28 08:52:40
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usually, filter is used to find a specific pattern in the pictures.
If we want to find lines and edges in the picture, firstly we think about gradients.
画图的时候,光画出线条就可以表达很多信息。
不要轻视线条,其中包含了太多的信息。

edges

线条的成因:
1. 物体表面的不连续,形状变化
2. 表面颜色不连续
3. 光照强度不连续
4. depth discontinuity,物体与背景的差异
[cv]edge detection: gradients

detection edges

recall images as functions.
each location (x,y) has a value.
[cv]edge detection: gradients
in the above picture, edges like the steep cliffs.
so, our basic idea: find a neighborhood with strong signs of change.
But, there are two problems:
1. neighborhood size
2. how to detect changes

derivative and edges

[cv]edge detection: gradients
differential operator
[cv]edge detection: gradients
image gradient
[cv]edge detection: gradients

the gradient direction is given by : tan1(fy/fx)
The amount of change is given by the gradient magnitude:f=(fx)2+(fy)2
[cv]edge detection: gradients

finite difference

fxf(x+1,y)f(x,y)1
it was called right derivative.
[cv]edge detection: gradients

differential operator

[cv]edge detection: gradients

sobel operator

[cv]edge detection: gradients
in matlab, there is a function imgradientxy which uses sobel operator but isn’t divided by 8.
So you need add a step to get that output divided by 8.

imgradientxy - Directional gradients of an image
This MATLAB function returns the directional gradients, Gx and Gy, the same size
as the input image I.
[Gx,Gy] = imgradientxy(I)
[Gx,Gy] = imgradientxy(I,method)
[gpuarrayGx,gpuarrayGy] = imgradientxy(gpuarrayI,_)

[cv]edge detection: gradients
[cv]edge detection: gradients

filter = fspecial('sobel'); % y direction
res = imfilter(double(img), filter);
imagesc(res);
colormap gray;
imshow(res);

[cv]edge detection: gradients

imfilter function use correlation by default.

% Gradient Direction
function result = select_gdir(gmag, gdir, mag_min, angle_low, angle_high)
    % TODO Find and return pixels that fall within the desired mag, angle range
    result = gmag>= mag_min & gdir >= angle_low & gdir <= angle_high;
endfunction

pkg load image;

%% Load and convert image to double type, range [0, 1] for convenience
img = double(imread('octagon.png')) / 255.; 
imshow(img); % assumes [0, 1] range for double images

%% Compute x, y gradients
[gx gy] = imgradientxy(img, 'sobel'); % Note: gx, gy are not normalized

%% Obtain gradient magnitude and direction
[gmag gdir] = imgradient(gx, gy);
imshow(gmag / (4 * sqrt(2))); % mag = sqrt(gx^2 + gy^2), so [0, (4 * sqrt(2))]
imshow((gdir + 180.0) / 360.0); % angle in degrees [-180, 180]

%% Find pixels with desired gradient direction
my_grad = select_gdir(gmag, gdir, 1, 30, 60); % 45 +/- 15
imshow(my_grad);  % NOTE: enable after you've implemented select_gdir

[cv]edge detection: gradients

[cv]edge detection: gradients

[cv]edge detection: gradients

[cv]edge detection: gradients

[cv]edge detection: gradients
[cv]edge detection: gradients
[cv]edge detection: gradients

[cv]edge detection: gradients