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BildanalyseBildanalyse
Image AnalysisImage Analysis
Paul BonsPaul BonsAG StrukturgeologieAG Strukturgeologie
AB Mineralogie und GeodynamikAB Mineralogie und Geodynamik
HHöölderlinstrlderlinstr. 16. 16
E-mail: [email protected]: [email protected]
URL: URL: http://structural-geology.infohttp://structural-geology.info
0160-55154820160-5515482
This course
• Overview of techniques for
• Image processing
• Image analysis
• Image understanding
• Focussing on digital methods
• Software to use:
• ImageJ
• Nat. Inst. Health (USA)
• http://rsb.info.nih.gov/ij/
Processing, analysis & understanding
• Processing
• Manipulation of (raw) image• Enhance image, reduce noise, etc.
• Prepare for analysis
Enhance contrast
Processing, analysis & understanding
• Analysis
• Extract data from image• Measurement, segmentation, etc.
Segmentation
Processing, analysis & understanding
• Understanding
• Convert data to information• Interpretation, classification of data
• Visualisation of data
Urban areas
This lecture: introduction
• What is an image?
• Basics of digital images
• Introduction to image operations
What is an image?
• An image is a visual projection or map of something(in the real world) onto some medium
• Projection method
• Photo camera
• Electron microscope
• Radar
• Satellite
• Scanner
• Medium
• Celluloid film
• Chip, computer RAM
• Photographic paper
Real world image
camera
Digital image acquisition
• Classical image acquisition captures three visiblelight bands
• Red
• Green
• Blue
Other acquisition methods
• Image acquisition not limited to 3 bands
• Electon microscopy: only one band
• Intensity of secondary electron emission
• Multi-spectral cameras: many bands
• For example ASTER satellite images:• 4 visible light bands
• 8 short-wavelength infrared bands
• However, the human eye can only see colour imagesmade of the three basic colours
Digital image = intensity map
• Light intensity is mapped onto a square grid or arrayof pixels
x
y
pixel3-dimensional "images": voxel
Number of pixels determinesresolution
• More pixels:
• Higher resolution (better quality)
• Bigger size• More memory space
• Slower calculations
128 x 128
49 kbyte
64 x 64
12 kbyte
32 x 32
3 kbyte
16 x 16
768 byte
8 x 8
192 byte
Monochrome (grey-scale) image
• Each pixel has a single value
• Represents the brightness orlight intensity at that position
• Standard convention
• Black = 0 (0% intensity)
• White = 255 (100% intensity)
• 256 possible intensity values
= 1 byte (256=28)X = 40
Y = 228Intensity = 168 (66%)
Number of grey levels
• The human eye is not verysensitive to grey levels
• We can hardly distinguish morethan 16 grey levels
256 levels 32 levels 16 levels
8 levels
Clear quality loss
Band width
• Number of grey levels determinesnumber of divisions between blackand white
• However, what is "black" and whatis "white"?
Imagebrightness
255
0
Saturatedwhite
Saturatedblack True brightness
BAND WIDTH Saturation
Colour images
• The human eye has three sensitivity peaks
• RED (!600 nm wavelength)
• GREEN (!550 nm wavelength)
• BLUE (!440 nm wavelength)
• Standard colour images are therefore RGB
• Alternative CMY
• Cyan-Magenta-Yellow
RED
GREENBLUE
YELLOW
CYAN
MAGENTA
Different bands, same result
colour red green blue
= + +
= + +
cyan magenta yellowPer pixel
3 byte 1 byte 1 byte 1 byte= + +
HSI: Hue-Saturation-Intensity
• Hue = colour (deviation from red)
• Saturation = colour intensity (deviation from grey)
• Intensity = brightness (same as grey-scale image)
• From RGB-colour image to grey-scale image
• Intensity = (R+G+B) / 3
colour hue saturation intensity
= + +
Combining other bands
band 1 band 2 band 3
Short-wavelengthinfrared(>0.72 µm)
• Some sensors recordmore than 3 bands
• Any three bands canbe combined to makea false-colour image
Band 1+2+3
Image operations
• Image operations are operations that convert oneimage to another
• Each pixel in the image is changed to a new value(monochrome) or more values (colour)
• Example: contrast enhancement
3 types of single image operations
• Point
• New value at (x,y)
depends only on original
value at (x,y)
• Local
• New value at (x,y)
depends on original
values around (x,y)
• Global
• New value at (x,y)
depends on all values in
original imageoriginal new
Operations can also involve multipleimages
• All arithmetic operations are possible on two or moreimages
• V(x,y,A) + V(x,y,B) = V(x,y,C)
• V(x,y,A) - V(x,y,B) = V(x,y,C)
• V(x,y,A) x V(x,y,B) = V(x,y,C)
• Etc.
+ =
Image A Image B Image C
End of lecture 1
• If you have a computer/laptop
• Get ImageJ (it's for free!)
• http://rsb.info.nih.gov/ij/
• Play with the program
• Make sure you have an account on the CIP-Poolcomputers
• Register with assistant
• Get a key-card to have access to room at any time