2. Information that lets you recognise a region.
Region description
Information that lets you recognise a region.

3. Introduction
Region detection isolates regions that differ from neighbours
Description identifies property values
Labelling identifies regions
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4. Contents Features derived from binary images Texture Surface shape
Structure
Region (CCA)
Shape
Texture
Surface shape
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5. Features derived from binary images
Connected component analysis
Perimeter
Area
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6. Connected Component Analysis
To identify groups of connected pixels
To label separate regions
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7. Algorithm ? 3 2 1 4 First pass If zero neighbours have a label
Pixel receives the next free label
If one or more neighbours have same label
Pixel receives same label;
If two or more neighbours have different labels
Pixel receives one label, equivalence is recorded
Second pass
Relabel all equivalent labels
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8. Borders Straight lines Curved lines Phi-S Snakes Chain codes Polylines
Splines
Circles
Phi-S
Snakes
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9. Chain Codes Trace the object outline - follow pixels on boundary
Code directions of movement
Description is
position independent,
orientation dependent
Can use differential chain codes 1 2 3 4 5 6 7
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10. Perimeter From Chain Code
Even codes have length 1
Odd codes have length 2
Perimeter length = #even + 2 #odd
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11. Area From Chain Code 1 2 3 4 5 6 7 h h+1/2 h h-1/2 -h-1/2 -h -h+1/21 2 3 4 5 6 7 h
h+1/2 h
h-1/2
-h-1/2 -h
-h+1/2
h is measured from an arbitrary datum,
e.g. y co-ordinate of start of codes.
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12. Crack Codes These follow pixel boundaries
Not pixel centres
Same representation of displacement
Longer coding
More accurate
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14. Demo
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15. Polyline Representation
Represent the line by a set of joined line segments
Polyline and original endpoints coincide
Segments interpolate edge points
Computed by curve splitting or segment merging
Decomposing initial curve
Combining curve segments
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16. Polyline Splitting (cf Hopalong last week)
For each curve segment
D = maximum distance of segment to line between endpoints
If D > threshold
Insert a vertex
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17. Segment Merging
May be necessary between endpoints of adjacent segments
Use edge following techniques
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18. Curved Line Sections
Polyline representation is suitable for linear sections
Curved sections are inefficiently represented
Alternatives
Splines
Circles
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19. B-Splines A curve represented by control points
Endpoints fixed by two control points
Shape controlled by two control points
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20. If control points can be found
Curve is compactly represented
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21. Fourier Descriptors
Represent co-ordinates of boundary points as complex numbers
They can be Fourier transformed
Coefficients of transform are the Fourier descriptors
Retain more or fewer according to desired accuracy
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22. Example
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27. Phi-S Curves (i, si) characteristic of the object’s shape s
independent of location
dependent on orientation
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29. Snakes, Active/Dynamic Contours
Borders follow outline of object
Outline obscured?
Snake provides a solution
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30. Algorithm Snake computes smooth, continuous border Minimises
Length of border
Curvature of border
Against an image property
Gradient?
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31. Minimisation Initialise snake Integrate energy along it
Iteratively move snake to global energy minimum
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33. Texture Two definitions
A pseudoregular arrangement of a primitive element
A pseudorandom distribution of brightness values
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34. Examples
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35. Classification A useful property for identifying surfaces
Aerial photographs
Medical imagery
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36. Structural Texture Representations
Require
Texture primitive - texel
Placement rule
Ideal for regular - man-made - textures
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37. Fourier Descriptors Placement rule  periodicity Can use
Autocorrelation
Fourier transform
To recognise it
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38. Fourier Descriptor Compute modulus of transform
Energy in different regions is characteristic of texture
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39. Markov Random Field Representations
Each pixel value a combination of neighbours plus noise
Find coefficients of model
Characterise texture
Least squares minimisation
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40. Statistical Descriptions
Better suited to pseudorandom, natural textures
First Order statistics
Second order statistics
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41. First Order Statistics
Statistical descriptions of grey level distribution
Mean grey value
Deviation of grey values
Coefficient of variation
etc.
Can give useful results
Generally too sensitive to factors other than identity of surface
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42. Second Order Statistics
Measures involving multiple pixels
Joint difference histogram
Histogram of differences between adjacent pixels
Co-Occurrence matrices
Measure frequency of specific pairs of grey values
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43. Co-Occurrence Matrices
Define a relative separation vector
e.g. 3 pixels across, 2 up
Use each pair of pixels separated by the vector as matrix indices
Increment this matrix element
Shape of matrix characterises the texture
Can be characterised by factors derived from it.
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44. Edge Frequency Density of microedges is characteristic of texture
Apply an edge detector
Sobel is suitable
Threshold result
Compute density of edge elements
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46. Shape from … To recover shapes of objects in a scene
By identifying spatial properties of surface patches
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47. Shape from Motion From Of Can compute 4 views 3 non-colinear points
motion and relative locations of points
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48. Shape from Photometric Stereo
Capture images of a scene in two cameras
Must know
Cameras’ separation
Cameras’ relative orientation (parallel in example)
Co-ordinates of corresponding points in images
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49. Plan view of cameras’ optical paths.
centres
Image
plane
Scene
(x, y,z)
camera 1 x
(x’, y’, f) d
centre
line
x+d d
camera 2 z f
(x’’, y’’, f)
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51. corresponding points are identified The point’s depth can be computed.
Provided that
cameras are aligned
separation is known
corresponding points are identified
The point’s depth can be computed.
Correspondence problem examined later.
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52. Shape from Shading
For matt surfaces, proportion of incident light reflected depends on
Surface reflectance
Surface orientation with respect to light source
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53. Can estimate cos q, hence q throughout image.
If k can be estimated
Image value for q = 0
Can estimate cos q, hence q throughout image.
Surface orientation is not determined uniquely
Two angles are needed
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54. Shape from Texture
Apparent texture of a surface is dependent on the surface’s
Orientation
Range
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55. Method Must be able to identify fundamental texture elements
Assume they are invariant
Compute mapping to transform each element to a standard appearance
Mapping determines surface orientation.
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56. Summary Binary image features Texture Shape from … Skeleton Boundaries
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57. There is no reason why anyone would want a computer in their home
Ken Olsen, chairman DEC, 1977
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