Transcript Image Processing

Image Processing
Segmentation
1.
2.
Process of partitioning a digital image into
multiple segments (sets of pixels).
Clustering pixels into salient image regions, i.e.,
regions corresponding to individual surfaces,
objects, or natural parts of objects.
Image Processing
Segmentation
3.
Used to locate objects and boundaries (lines,
curves, etc.) in images.
4.
Process of assigning a label to every pixel in
an image such that pixels with the same label
share certain visual characteristics.
Image Processing
Segmentation
Two of the most common techniques:
thresholding
and
edge finding
Image Processing
Segmentation
Two of the most common techniques:
edge finding
thresholding
Image Processing
A. Threshold
The original
image:
The objects
in image:
A parameter  called the brightness threshold is
chosen and applied to the image f(x, y) as follows:
(x, y)  Object
or
 f(x, y)  
(x, y)  Object

f(x, y)  
Image Processing
A. Threshold
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".
Image Processing
A. Threshold
The original
image:
The objects
in image:
How to choose the threshold  ?
Image Processing
Fixed threshold
A threshold will be chosen independently of the image data
Image Processing
Fixed threshold
Source and segmented images with fixed threshold 128
Image Processing
Ảnh I:
Ảnh I đã được
phân đoạn với
ngưỡng 200 cho
thành phần RED:
R = 200
Image Processing
Ảnh I:
Ảnh I đã được
phân đoạn với
ngưỡng 170:
R = 170
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 algorithm
The threshold chosen by Isodata algorithm is 139
Isodata algorithm
Ảnh I:
Ảnh I đã được
phân đoạn với
ngưỡng 134:
 = 134,
k=2
Isodata algorithm
Ảnh I:
Ảnh I đã được
phân đoạn với
ngưỡng 128:
= 128,
k=1
Isodata algorithm
Ảnh I:
Ảnh I đã được
phân đoạn với
ngưỡng 116:
 = 116,
k=4
Isodata algorithm
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
 = 116,
k=4
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.
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.
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.
Image Processing
Triangle algorithm
Source and segmented images with threshold 152
chosen by triangle algorithm
Image Processing
Background-symmetry algorithm
dominant peak (183)
Assumes a distinct and dominant peak for the
background that is symmetric about its maximum.
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.
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.
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.
Image Processing
Background-symmetry algorithm
This means a threshold value given by
 = 183 - (216 - 183) = 150.
In formula:
Image Processing
Background-symmetry algorithm
Source and segmented images with fixed threshold 150
Một vài ứng dụng
Bài toán:
Input:
Output:
Ảnh I
Ảnh I có chứa đám lửa?
Một vài ứng dụng
Lời giải:
1. 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.
Một vài ứng dụng
Đặc trưng màu điểm ảnh thuộc phần 1 (đám lửa)(1):
R  G

G  B
R  

 =170
(a )
(b)
(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.
Một vài ứng dụng
Ảnh I:
Ảnh I đã được phân đoạn với ngưỡng
200 cho thành phần RED: