Image processing
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Image Processing. Segmentation Process of partitioning a digital image into multiple segments (sets of pixels). 2. Clustering pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. Image Processing. Segmentation

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Image Processing

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Image processing

Image Processing

Segmentation

Process of partitioning a digital image into multiple segments (sets of pixels).

2. Clustering pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects.


Image processing1

Image Processing

Segmentation

Used to locate objects and boundaries (lines, curves, etc.) in images.

Process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics.


Image processing2

Image Processing

Segmentation

Two of the most common techniques:

thresholding

and

edgefinding


Image processing3

Image Processing

Segmentation

Two of the most common techniques:

edgefinding

thresholding


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Image Processing

The original image:

The objects in image:

A parameter called the brightnessthreshold is chosen and applied to the image f(x, y) as follows:

(x, y)  Object f(x, y) 

or

(x, y)  Object  f(x, y)

A. Threshold


Image processing5

Image Processing

The original image:

The objects in image:

Remark:

The output is the label "object" or "background" which, due to its dichotomous nature, can be represented as a Boolean variable "1" or "0".

A. Threshold


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Image Processing

The original image:

The objects in image:

How to choose the threshold ?

A. Threshold


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Image Processing

A threshold will be chosen independently of the image data

Fixed threshold


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Image Processing

Fixed threshold

Source and segmented images with fixed threshold 128


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Ảnh I:

Ảnh I đã được phân đoạn với ngưỡng 200 cho thành phần RED:

R = 200

Image Processing


Image processing10

Ảnh I:

Ảnh I đã được phân đoạn với ngưỡng 170:

R = 170

Image Processing


Isodata algorithm

Isodata algorithm

  • This iterative technique for choosing a threshold was developed by Ridler and Calvard .

  • Set k = 0, 0 = L/2.

  • While |k-k-1|> 

    • 1.Compute the sample mean mf,k of the gray values associated with the foreground pixels and the sample mean mb,k of the gray values associated with the background.

    • 2. Compute a new threshold value k:

    • k = ( mf,k-1 + mb,k-1 ) / 2

    • 3. k = k +1


Isodata algorithm1

Isodata algorithm

The threshold chosen by Isodata algorithm is 139


Isodata algorithm2

Ảnh I:

Isodata algorithm

Ảnh I đã được phân đoạn với ngưỡng 134:

 = 134,

k=2


Isodata algorithm3

Ảnh I:

Isodata algorithm

Ảnh I đã được phân đoạn với ngưỡng 128:

  • = 128,

    k=1


Isodata algorithm4

Ảnh I:

Isodata algorithm

Ảnh I đã được phân đoạn với ngưỡng 116:

 = 116, k=4


Isodata algorithm5

m1 = 0; m2 = L;

teta = (m1 + m2) / 2

stop = false

while !stop

ts1 = 0; ts2 = 0

ms1 = 0; ms2 = 0

for i = 0 to teta

ts1 = ts1 + h(i) * i

ms1 = ms1 + h(i)

m1 = ts1/ms1

for i = teta to L

ts2 = ts2 + h(i) * i

ms2 = ms2 + h(i)

m2 = ts2/ms2

tg = Round((m1 + m2) / 2)

if teta - tg  < 

stop = true

teta = tg

loop

Isodata algorithm

 = 116, k=4


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Image Processing

Triangle algorithm

A line is constructed between the maximum of the histogram at brightness bmax and the lowest value bmin = (p=0)% in the image.


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Image Processing

Triangle algorithm

The distance d between the line and the histogram h[b] is computed for all values of b from b = bmin to b = bmax.


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Image Processing

Triangle algorithm

The brightness value bo where the distance between h[bo] and the line is maximal is the threshold value, that is, = bo.


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Image Processing

Triangle algorithm

Source and segmented images with threshold 152 chosen by triangle algorithm


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dominant peak (183)

Image Processing

Background-symmetry algorithm

Assumes a distinct and dominant peak for the background that is symmetric about its maximum.


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Image Processing

Background-symmetry algorithm

  • The maximum peak (maxp) is found by searching for the maximum value in the histogram.

  • Searching on the non-object pixel side of that maximum to find a p% point.


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Image Processing

Background-symmetry algorithm

the object pixels are located to the left of the background

peak at brightness 183, that mean

h(183) = max {h(a): 0  h(a)  255 } = 351

search on the right of that peak to locate to find 95%. The total number of pixels in the image is 17424 and the total number of pixels on the right of peak is 8241, about 95% (94.59%) of 17424/2 = 8712.


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Image Processing

Background-symmetry algorithm

At which brightness value 5% of the pixels lie to the right (are above)? This occurs at brightness 216. The number of pixels on the right of 216 is 936, equal to 5% (0.0537) the total number of pixels in the image: 17424.

Because of the assumed symmetry, we use as a threshold a displacement to the left of the maximum that is equal to the displacement to the right where the p% is found.


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Image Processing

Background-symmetry algorithm

This means a threshold value given by

 = 183 - (216 - 183) = 150.

In formula:


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Image Processing

Background-symmetry algorithm

Source and segmented images with fixed threshold 150


M t v i ng d ng

Một vài ứng dụng

Bài toán:

Input: Ảnh I

Output: Ảnh I có chứa đám lửa?


M t v i ng d ng1

Một vài ứng dụng

  • Lời giải:

  • Phân vùng ảnh I thành 2 phần: Phần 1 gồm những điểm ảnh thuộc đám lửa và phần 2 gồm những điểm ảnh không thuộc đám lửa.

  • 2. Nếu diện tích phần 1 lớn hơn một ngưỡng nào đó (ví dụ, có ít nhất vài điểm ảnh) thì kết luận là ảnh I có chứa đám lửa.


M t v i ng d ng2

 =170 (b)

Một vài ứng dụng

Đặc trưng màu điểm ảnh thuộc phần 1 (đám lửa)(1):

(1) Đào Thanh Tĩnh, Hà Đại Dương, Một mô hình phát hiện đám cháy qua ảnh video, Tạp chí Khoa học và Kỹ thuật, ISSN-1859-0209, tr. 5-11, Số 127, 4-2009.


M t v i ng d ng3

Ảnh I:

Một vài ứng dụng

Ảnh I đã được phân đoạn với ngưỡng 200 cho thành phần RED:


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