Digital Image Processing Lecture Segmentation
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Transcript of Digital Image Processing Lecture Segmentation
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Digital Image Processing
Segmentation
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Digital Image Processing
Segmentation Statistical methods Edge-based methods Region based methods
Tresholding Growing regions Clustering Split/Merge
Morphological methods watershed Knowledge-based Template matching....
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Digital Image Processing
Segmentation is a complex task... Different tasks specifics methods No universal method for all tasks ! Example features which make it difficult:
Important factor quality of the image Complex shapes of the objects Overlapping objects Shadows Pre-processing could be necessary
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Digital Image Processing
Thresholding
? Threshold value?
Bimodal histogram
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Digital Image Processing
Thresholding
Multimodal Histogram
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Digital Image Processing
Otsu's method
Otsus thresholding chooses the threshold to minimize the intraclass variance and maximize the interclass variance between a segmented-foreground object and background
All possible thresholds (0-255..) are evaluated in this way, and that one that maximises the criterion is chosen as the optimal threshold
Recurcive method
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Digital Image Processing
Global / local / adaptive thresholding In global thresholding, the threshold value is
held constant throughout the image Global thresholding is of the form:
Local threshold > eliminate changes in illumination
Adaptive thresholding - technique in which the threshold level is variable.
g(x,y) = 0 f(x,y) < Threshold
1 f(x,y) >= Threshold
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Digital Image Processing
Global binary threshold versus adaptive binary threshold
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Digital Image Processing 9
Segmentation based on edge detection
Segmentation based on edge detection Determination of object boundaries from a edge
image (Sobel, Laplace, Canny detector...) Problems:
Edges are overlapped, not continuous... Non relevant edges have to be avoid
Pre-processing and post-processing could be necessary...
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Digital Image Processing 10
Gray level profile
The 1st derivative
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5-0.20
0.20.40.60.8
11.2
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5-0.06-0.04-0.02
00.020.040.06
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5-5-4-3-2-1012345 x 10
-3
Edge Edge
Minimum point
Maximumpoint
+ +- -
Zero crossing
Inte
nsity
The 2nd derivative
Edge detection
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Digital Image Processing 11
An Example - Results of Canny edge detection
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Digital Image Processing 12
Active contour model- snake
The active contour model, or snake, is defined as an energy minimizing spline - the snake's energy depends on its shape and location within the image.
Local minima of this energy then correspond to desired image properties.
a) initial snake position
b-c) iteration steps of snake energy minimization
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Digital Image Processing 13
Growing regions Select a start (seed) point Grow the point based on a certain property of
homogeneity (Connectivity should be considered) Seed point selection:
..for example - highlighted point (Due to specific property) ...
. (cvFloodFill)
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Digital Image Processing 14
An example of growing regions
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Digital Image Processing 15
Clustering
Consider segmentation as a classification problem : An image consists of K regions. Each pixel have to be classified into the one of
this regions. K-Means algorithms Mean Shift algorithms Fuzzy C-Means Gaussian Mixture Models and much more...
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Digital Image Processing 16
K-Means Algorithm
Each region is represented by a mean value j. Minimum distance classification
1D
2D
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Digital Image Processing 17
K-Means Algorithm
Assume a fixed number k of segments/clusters. Represent each pixel in a 1D [luminance] or 3D
[luminance, x, y] vector space. Step 1. random initialisation of k mean values j. Step 2. Classify pixels into the regions using the
minimum distance criterion Step 3. Calculate new mean values of the
regions Repeat 2 and 3 until the mean values are stable
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Digital Image Processing 18
Example of K-Means Algorithm
2 regions2 regions
3 regions
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Digital Image Processing 19
K-Means Algorithm
If the K-means algorithm use only 1 feature (lightness), than is this operation only a modification of the thresholding method
Modifikation -> extended feature vector: Colour information Position of the pixel Texture description etc.
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Digital Image Processing 20
Mean Shift Algorithm
Choose a search window size. Choose the initial location of
the search window. Compute the mean location
(centroid of the data) in the search window.
Center the search window at the mean location computed in Step 3.
Repeat Steps 3 and 4 until convergence.
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Digital Image Processing 21
Mean Shift Algorithm
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Digital Image Processing 22
Mean Shift Segmentation Algorithm 1. Convert the image into the features ( color,
gradients, texture measures etc). 2. Choose initial search window locations
uniformly in the data. 3.Compute the mean shift window location for
each initial position. 4.Merge windows that end up on the same peak
or mode. 5.The data these merged windows traversed are
clustered together.
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Digital Image Processing 23
Mean Shift Algorithm(Features: L*u*)
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Digital Image Processing 24
Mean shift segmentation example
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Digital Image Processing 25
Split & Merge AlgorithmStep 1 SPLIT Recursively splits an image region (which starts
out as the entire image) by dividing it into quarters. Feature vectors are computed for each block.
If the four blocks are judged to be similar, they are grouped back together and the process ends.
If not, each of the blocks are recursively divided and analyzed using the same procedure.
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Digital Image Processing 26
Split & Merge Algorithm
When we are finished, there will be many different sized blocks, each of which has a homogeneous texture according to our model.
However, the arbitrary division of the image into rectangles (called a quad-tree'' decomposition) might have accidentally split up regions of homogeneous texture.
The merge step, then, tries to fix this by looking at adjacent regions that were not compared to each other during the split phase, and merging recursively splits an image region (which starts out as the entire image) by dividing it into quarters.
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Digital Image Processing 27
Split & Merge AlgorithmStep 2 - MERGE Feature vectors are computed for each block. If the four blocks are judged to be similar, they are
grouped back together and the process ends. If not, each of the blocks are recursively divided
and analyzed using the same procedure.
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Digital Image Processing
Split & Merge -Example
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Digital Image Processing 29
Synthetically generated image of two dark blobs
Watershed Segmentation The watershed transform treats the intensity as a
function defining 'hills' and 'valleys' and attempts to detect the valleys.
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Digital Image Processing 30
Watershed Segmentation
B Distance transform
D Watershed
C Image's complement
://httpwww.mathworks.de/company/newsletters/news_notes/win02/watershed.html
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Digital Image Processing 31
Watershed Segmentation on image gradient
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Digital Image Processing 32
Over-segmentation may occur, especially in noisy images (gradient enhances the high frequencies!)
A preprocessing with a smoothing filter can reduce noise and remove small details
Watershed Segmentation
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Digital Image Processing
Match-based segmentationTemplate segmentation
Evaluate a match criterion for each location and rotation of the pattern in the image
Local maxima of this criterion exceeding a present threshold represent pattern location in the image Matching criteria can be defined in many
ways; in particular, correlation between a pattern and the searched image data is a general matching criterion.
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Digital Image Processing 34
Template matchingCorrelation
The operation called correlation is closely related to convolution.
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Digital Image Processing 35
Template matching by correlation
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Digital Image Processing 36
Demo of template matching using correlation
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Digital Image Processing
Demo of template matching using correlation
http://bigwww.epfl.ch/demo/templatematching/tm_correlation/demo.html
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Digital Image Processing
Other Features-based Segmentation
Frequency features ... successful in the tasks of texture
segmentation ....
PCA - Principal component analysis SIFT - Scale-invariant feature transform
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Digital Image Processing
Segmentation in Video-sequences
Background Subtraction first must be learned a model of the
background this background model is compared against
the current image and then the known background parts are subtracted away.
The objects left after subtraction are presumably new foreground objects.
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Digital Image Processing 40
Establishing a Reference Images
Establishing a Reference Images: Difference will erase static object: When a dynamic object move out completely from its
position, the back ground in replaced.
SegmentationFolie 2Folie 3 ThresholdingFolie 5Folie 6Folie 7Folie 8Edge DetectionSlide 4Folie 11Folie 12Folie 13An example of growing regionsFolie 15Folie 16Folie 17Folie 18Folie 19Folie 20Folie 21Folie 22Folie 23Folie 24Folie 25Folie 26Folie 27Split & Merge -ExampleFolie 29Folie 30Slide 32Slide 33Folie 33Object recognition CorrelationMatching by correlationFolie 36Folie 37Folie 38Folie 39Establishing a Reference Images