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Segmentation. Grouping and Segmentation. Grouping and Segmentation appear to be one of the early processes in human vision They are a way of *organizing* image content into “semantically related” groups

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segmentation

Segmentation

Copyright G.D. Hager

grouping and segmentation
Grouping and Segmentation
  • Grouping and Segmentation appear to be one of the early processes in human vision
  • They are a way of *organizing* image content into “semantically related” groups
  • In some applications, segmentation is the crucial step (e.g. some types of aerial image interpretation).

Copyright G.D. Hager

grouping and segmentation3
Grouping and Segmentation
  • Grouping is the process of associating similar image features together

Copyright G.D. Hager

grouping and segmentation4
Grouping and Segmentation
  • Grouping is the process of associating similar image features together
  • The Gestalt School:
    • Proximity:tokens that are nearby tend to be grouped.
    • Similarity:similar tokens tend to be grouped together.
    • Common fate:tokens that have coherent motion tend to be grouped together.
    • Common region:tokens that lie inside the same closed region tend to be grouped together.
    • Parallelism:parallel curves or tokens tend to be grouped together.
    • Closure:tokens or curves that tend to lead to closed curves tend to be grouped together.
    • Symmetry:curves that lead to symmetric groups are grouped together.
    • Continuity:tokens that lead to “continuous ” (as in “joining up nicely ”, rather than in the formal sense): curves tend to be grouped.
    • Familiar Conguration: tokens that, when grouped, lead to a familiar object,tend to be grouped together

Copyright G.D. Hager

grouping and segmentation8
Grouping and Segmentation
  • Segmentation is the process of dividing an image into regions of “related content”

Courtesy Uni Bonn

Copyright G.D. Hager

grouping and segmentation9
Grouping and Segmentation
  • Segmentation is the process of dividing an image into regions of “related content”

Courtesy Uni Bonn

Copyright G.D. Hager

grouping and segmentation10
Grouping and Segmentation
  • Both are an ill defined problem --- related or similar is often a high-level, cognitive notion
  • The literature on segmentation and grouping is large and generally inconclusive --- we’ll discuss a couple of algorithms and an example.

Copyright G.D. Hager

simple thresholding
Simple Thresholding
  • Choose an image criterion c
  • Compute a binary image by b(i,j) = 1 if c(I(i,j)) > t; 0 otherwise
  • Perform “cleanup operations” (image morphology)
  • Perform grouping
    • Compute connected components and/or statistics thereof

Copyright G.D. Hager

an example motion
An Example: Motion

Detecting motion:

Copyright G.D. Hager

thresholded motion
Thresholded Motion

Detecting motion:

50

Candidate areas for

motion

Copyright G.D. Hager

a closer look
A Closer Look

Copyright G.D. Hager

color a second example
Color: A Second Example

Already seen in the tracking lecture

(quick recap)

Copyright G.D. Hager

homogeneous region photometry
Homogeneous Region: Photometry

PCA-fitted

ellipsoid

Sample

Copyright G.D. Hager

slide18

Binary Image Processing

After thresholding an image, we want to know usually the following about the regions found ...

How many objects are in the image?

Where are the distinct “object” components?

Copyright G.D. Hager

slide19

Connected Component Labeling

Goal: Label contiguous

areas of a segmented

image with unique labels

0

1

2

One uses a 4-neighbor or 8-neighbor connectivity

Copyright G.D. Hager

limitations of thresholding
Limitations of Thresholding
  • A uniform threshold may not apply across the image
  • It measures the uniformity of regions (in some sense), but doesn’t examine the inter-relationship between regions.
  • Local “disturbances” can break up nominally consistent regions

Copyright G.D. Hager

more general segmentation
More General Segmentation
  • Region Growing:
    • Tile the image
    • Start a region with a seed tile
    • Merge similar neighboring tiles in the region body
    • When threshold exceeded, start a new region
  • Region Splitting
    • Start with one large region
    • Recursively
      • Choose the region with highest dissimilarity
      • If sufficiently similar, stop, otherwise split
      • repeat until no more splitting occurs

Bottom-up approach

Top-down approach

Copyright G.D. Hager

another example image segmentation
Another Example: Image Segmentation
  • The goal: to choose regions of the image that have similar “statistics.”
  • Possible statistics:
    • mean
    • Variance
    • Histograms

Copyright G.D. Hager

an image histogram
An Image Histogram

Copyright G.D. Hager

how does one form a histogram
How does one form a histogram?
  • Let us consider a gray scale image for simplicity (image values ranging from 0-255)
  • Select the number of bins n (max 256 bins)
  • Width of each bin is 256/n. If n = 8, width = 32
  • Set counter for each bin to zero.
  • Now for each pixel, depending on its gray scale value, increment the counter of the bin where the gray scale value of the pixel falls.
  • The final counter values of the bins is the histogram of the image for the specified number of bins.

0-32

33-64

65-96

Copyright G.D. Hager

comparing histograms
Comparing Histograms

Copyright G.D. Hager

which is more similar
Which is More Similar?

.906

.266

Copyright G.D. Hager

more examples
More Examples

Copyright G.D. Hager

k means
Algorithm

Choose a fixed number of clusters and initial cluster centers

Allocate points to clusters that they are closest too

Recompute the cluster centers

Go back to step 2, and repeat until convergence

K-Means

Copyright G.D. Hager

slide30

Image

Clusters on intensity

Clusters on color

K-means clustering using intensity alone and color alone

Copyright G.D. Hager

an example blobworld carson belongie greenspan malik
An Example: BlobWorld(Carson, Belongie, Greenspan, Malik)

The problem: query images (e.g. from the WEB) using

image information

The solution: segment images into roughly uniform regions

and search based on feature vectors

The features:

color

texture

location (i.e. spatial compactness)

Copyright G.D. Hager

example segmentations
Example Segmentations

Copyright G.D. Hager

querying
Querying

User selects a weighting of color vs. texture giving

a diagonal weight matrix S

Given a blob with feature vector vi, compared to

another vector vj using Mahalanobis distance:

di,j = (vi – vj)tS (vi – vj)

Compound queries using min and max for and and or

Copyright G.D. Hager

more complex segmentation methods
More Complex Segmentation Methods
  • Snakes
  • Level Sets
  • Graph Cuts
  • Generalized PCA

Copyright G.D. Hager

snakes
Snakes

Copyright G.D. Hager

Images taken from http://www.cs.bris.ac.uk/home/xie/content.htm

level sets
Level Sets

Copyright G.D. Hager

Images taken from http://www.cgl.uwaterloo.ca/~mmwasile/cs870/

graph cuts
Graph Cuts

Copyright G.D. Hager

Images taken from efficient graph-based segmentation paper

gpca rene vidal
GPCA (Rene Vidal)

Human

GPCA

Copyright G.D. Hager