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Image%20Segmentation

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Title: Image%20Segmentation


1
Image Segmentation
  • Shengnan Wang
  • shengnan_at_cs.wisc.edu

2
Contents
  • Introduction to Segmentation
  • Mean Shift Theory
  • What is Mean Shift?
  • Density Estimation Methods
  • Deriving the Mean Shift
  • Mean Shift Properties
  • Application
  • Segmentation

Intelligent Computing Control Lab School of
Electrical Engineering at SNU
3
Motivation
  • What do we see in an image?
  • How is the image represented?
  • Goal Find relevant image regions for the objects
    we want to analyze

4
Image Segmentation
  • Definition 1 Partition the image into connected
    subsets that maximize some uniformity criteria.
  • Definition 2 Identify possibly overlapping but
    maximal connected subsets that satisfy some
    uniformity

5
Background Subtraction
6
Other Applications
  • Medical Imaging
  • Locate tumors and other pathologies
  • Measure tissue volumes
  • Computer-guided surgery
  • Diagnosis
  • Treatment planning
  • Study of anatomical structure
  • Locate objects in satellite images (roads,
    forests, etc.)
  • Face Detection
  • Machine Vision
  • Automatic traffic controlling systems

7
Methods
  • Clustering Methods
  • -K means, Mean Shift
  • Graph Partitioning Methods
  • -Normalized Cut
  • Histogram-Based Methods
  • Edge Detection Methods
  • Model based Segmentation
  • Multi-scale, Region Growing , Neural Networks,
    Watershed Transformation

8
Segmentation as clustering
  • Cluster together (pixels, tokens, etc.) that
    belong together
  • Agglomerative clustering
  • attach pixel to cluster it is closest to
  • repeat
  • Divisive clustering
  • split cluster along best boundary
  • repeat
  • Point-Cluster distance
  • single-link clustering
  • complete-link clustering
  • group-average clustering
  • Dendrograms(Tree)?
  • yield a picture of output as clustering process
    continues

From Marc Pollefeys COMP 256 2003
9
Mean Shift Segmentation
  • Perhaps the best technique to date

http//www.caip.rutgers.edu/comanici/MSPAMI/msPam
iResults.html
10
Mean Shift Theory
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
11
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
12
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
13
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
14
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
15
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
16
Intuitive Description
Region of interest
Center of mass
Mean Shift vector
Objective Find the densest region
Distribution of identical billiard balls
17
Intuitive Description
Region of interest
Center of mass
Objective Find the densest region
Distribution of identical billiard balls
18
1. What is Mean Shift?
  • Non-parametric density estimation

Assumption The data points are sampled from an
underlying PDF
Data point density implies PDF value !
Assumed Underlying PDF
Real Data Samples
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
19
1. What is Mean Shift?
  • A tool for
  • Finding Modes in a set of data samples,
    manifesting an underlying probability density
    function (PDF) in RN

Non-parametric Density Estimation
Discrete PDF Representation
Non-parametric Density GRADIENT Estimation
(Mean Shift)?
PDF Analysis
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
20
2. Density Estimation Method
  • Kernel Density Estimation

Intelligent Computing Control Lab School of
Electrical Engineering at SNU
21
2. Density Estimation Method
  • Kernel Density Estimation
  • - Various kernels

Intelligent Computing Control Lab School of
Electrical Engineering at SNU
22
3. Deriving the Mean Shift
Kernel Density Estimation
Gradient
Give up estimating the PDF ! Estimate ONLY the
gradient
Using the Kernel form
Function of vector length only
Define
Size of window
23
Kernel Density Estimation
Computing The Mean Shift
Gradient
24
Computing The Mean Shift
Yet another Kernel density estimation !
  • Simple Mean Shift procedure
  • Compute mean shift vector
  • Translate the Kernel window by m(x)
  • repeat

25
Mean Shift Mode Detection
What happens if we reach a saddle point ?
Perturb the mode position and check if we return
back
  • Updated Mean Shift Procedure
  • Find all modes using the Simple Mean Shift
    Procedure
  • Prune modes by perturbing them (find saddle
    points and plateaus)
  • Prune nearby take highest mode in the window

26
Real Modality Analysis
Tessellate the space with windows
Run the procedure in parallel
27
Real Modality Analysis
The blue data points were traversed by the
windows towards the mode
28
Mean Shift Algorithm
  • 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.

The mean shift algorithm seeks the mode or
point of highest density of a data distribution
29
4. Mean Shift Properties
  • Automatic convergence speed the mean shift
    vector size depends on the gradient itself.
  • Near maxima, the steps are small and refined
  • Convergence is guaranteed for infinitesimal
    steps only ? infinitely convergent, (therefore
    set a lower bound)?

Adaptive Gradient Ascent
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
30
4. Mean Shift Properties
  • Advantages
  • Application independent tool
  • Suitable for real data analysis
  • Does not assume any prior shape (e.g.
    elliptical) on data clusters
  • Can handle arbitrary feature spaces
  • Only ONE parameter to choose
  • h (window size) has a physical meaning,
    unlike K-Means
  • Disadvantages
  • The window size (bandwidth selection) is not
    trivial
  • Inappropriate window size can cause modes to
    be merged, or generate additional shallow
    modes ? Use adaptive window size

Intelligent Computing Control Lab School of
Electrical Engineering at SNU
31
Mean Shift Segmentation
  • Mean Shift Segmentation Algorithm
  • Convert the image into tokens (via color,
    gradients, texture measures etc).
  • Choose initial search window locations uniformly
    in the data.
  • Compute the mean shift window location for each
    initial position.
  • Merge windows that end up on the same peak or
    mode.
  • The data these merged windows traversed are
    clustered together.

Image From Dorin Comaniciu and Peter Meer,
Distribution Free Decomposition of Multivariate
Data, Pattern Analysis Applications
(1999)22230
32
Discontinuity Preserving Smoothing
Feature space Joint domain spatial
coordinates color space
Meaning treat the image as data points in the
spatial and range (value) domain
Image Data (slice)
Mean Shift vectors
Smoothing result
Mean Shift A robust Approach Toward Feature
Space Analysis, by Comaniciu, Meer
33
Discontinuity Preserving Smoothing
The image gray levels
can be viewed as data points in the x, y, z
space (joined spatial And color space)
34
Discontinuity Preserving Smoothing
Flat regions induce the modes !
35
Discontinuity Preserving Smoothing
The effect of window size in spatial and range
spaces
36
Segmentation
Segment Cluster, or Cluster of Clusters
  • Algorithm
  • Run Filtering (discontinuity preserving
    smoothing)
  • Cluster the clusters which are closer than
    window size

Image Data (slice)
Mean Shift vectors
Segmentation result
Smoothing result
Mean Shift A robust Approach Toward Feature
Space Analysis, by Comaniciu, Meer http//www.caip
.rutgers.edu/comanici
37
Mean Shift SegmentationResults
http//www.caip.rutgers.edu/comanici/MSPAMI/msPam
iResults.html
38
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39
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
40
Intelligent Computing Control Lab School of
Electrical Engineering at SNU
41
K-means
Mean Shift
Max Entropy Threshold
Normalized Cut
42
Mean Shift questions
  • What happens if we change the size of the search
    window?
  • What do we need to know to calculate every step?
  • How can we improve the performance?
  • How many segments will we have?
  • Will the segments be connected?

43
Questions?
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