Point Processing Lecture on the image part

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Point Processing Lecture on the image part
(2)Automatic Perception (AP2)

• Thomas Moeslund
• Computer Vision and Media Technology lab.
• Aalborg University
• tbm_at_cvmt.dk

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Topics

• What is point processing?
• Grey level mapping
• Histograms
• Segmentation using thresholding

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What is point processing?

• Grey level enhancement
• Process one pixel at a time independent of all
  other pixels
• For example used to correct Brightness and
  Contrast (remote control)

Too low contrast
Too high contrast
Too high brightness
Too low brightness
Correct
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Brightness

• The brightness is the intensity
• Change brightness
• To each pixel is added the value b
• f(x,y) is the input image
• g(x,y) is the (enhanced) output image
• If bgt0 gt brighter image
• If blt0 gt less bright image

g(x,y) f(x,y) b
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Contrast

• The contrast describes the level of details we
  can see
• Change contrast
• Each pixel is multiplied by a
• f(x,y) is the input image
• g(x,y) is the (enhanced) output image
• If agt1 gt more contrast
• If alt1 gt less contrast

g(x,y) a f(x,y)
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Combining brightness and contrast
g(x,y) a f(x,y) b

• Both
• A straight line
• Greylevel mapping
• X-Axis Input Value
• Y-Axis Output Value
• This plot Identity
• Output equals Input
• Apply to each pixel!
• To save time the greylevel
• mapping can be written as a
• Lookup-Table

Output image g(x,y)
Input image f(x,y)
(Show bridge)
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Histogram
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How to set the greylevel mapping
k

• Histogram processing a powerfull tool!

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Histogram Types
Dark Image
Bright Image
Low Contrast
High Contrast

• How can a histogram help? (show PET_scan
  TEM_filter)

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Histogram processing

• Different types of mapping
• Piecewise linear

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Histogram processing

• Non-linear, e.g., Logarithmic
• Arbitrary

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Improving contrast

• Humans cannot tell the difference between
  greylevel values too close to each other
• So spread out the greylevel values
• This is called histogram stretching

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Histogram stretching

• Algorithm
• Find maximum
• Find minimum
• Shift values so that minimum is 0
• Scale values so that maximum is 255 and write
  into output image g(x,y)

max0 //Check each pixel if f(x,y)gtmax maxf(x
,y)
//For each pixel g(x,y)f(x,y)-min
c255/(max-min) //For each pixel g(x,y)round(g(
x,y)c)
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Histogram Equalization
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Something really different
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Segmentation

• Until now Image processing (manipulation)
• Image analysis segmentation
• The task
• Information versus noise
• Foreground (object) versus background

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Segmentation

• Use greylevel mapping and the histogram
• When two peaks (modes) of a histogram correspond
  to object and noise (Show AuPbSn40, bridge)
• Find a THRESHOLD value, T, that separates the two
  peaks. This process is called THRESHOLDING
• Algorithm
• If f(x,y) gt T then g(x,y) 1, else g(x,y) 0
• If f(x,y) lt T then g(x,y) 1, else g(x,y) 0
• Result a binary image where
• object pixels 1 and noise 0

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What to remember

• Point processing
• Brightness and contrast
• Greylevel mapping
• Histogram
• Thresholding (segmentation)

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Exercises

• Questions to the lecture?
• What was good about the lecture and what could
  have been better?
• Discuss the PE-questions
• Download ImageJ http//rsb.info.nih.gov/ij/
• Use ImageJ to play around with greylevel mapping,
  histograms and thresholding. Make sure you
  understand these concepts!
• How will the algorithm look like if two threshold
  values are used instead of just one?
• Is it wise to have a binary image consisting of
  0s and 1s?
• Finish the exercises from last time