# Image Processing

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```					       Image Processing

Rule 1: Always save your primary data
Rule 2: Be able to describe
quantitatively what you have done
3 types of operations
• Point operations: operate on a pixel-by-pixel
basis
• Neighborhood operations: operate on a small
group of adjacent pizels
• Reciprocal space operations: deal with image-
wide patterns and chartacteristics
Point operations (Black-and-White)

http://homepages.inf.ed.ac.uk/rbf/HIPR2/pnto
ps.htm

Thresholding
Gamma Curves
Histogram Equalization
Point Operations (Color)
• Color maps
– RGB (More common in scientific imaging)
– CMYK (Printers, etc)
• Color Balance
• HSV
• Two sources of shading variation in images
– Dye binding to background: Image subtraction is
appropriate
– Camera/light source nonuniformity: Image division is
appropriate
• Image subtraction
• Image division: Divide data image by blank image and
normalize
• If you don’t have a blank image: erode features and
smooth to derive background (“flatten image” in Image
Pro Plus)
Geometric correction
• Geometric distortion: pincushion and barrel
distortion
• Geometric distortion: trapezoidal distortion
• Tiepoints: set of points with known geometric
relationships to each other
• Set up a matrix of actual geometric positions
in the image as a function of pixel coordinates
• Interpolate beween nonintegrap pixels
positions to get square pixels.
Neigborhood operations – allow
feature extraction
• Convolution operator: a matrix that applies a
kernel (say 3X3) to every point in the image
• Replaces the central point by the resultant of
multiplying that 3X3 matrix by neighboring
pixels

Molecular Expressions
Web Site
Averaging kernel
• Replace central point with average of
neighborhood
1/9                   1/9                  1/9

1/9                   1/9                  1/9

1/9                   1/9                  1/9

Most software packages do the normalization automatically, so you can
use “1”’s instead of “1/9”’s
Smoothing kernel
• Gaussian (3X3)
1            4         1
4            12        4
1            4         1

• (5x5)

1        2    3        2        1
2        7    11       7        2
3        11   17       11       3
2        7    11       7        2
1        2    3        2        1
Sharpening kernels
• Laplacian
-1                    -1                   -1
-1                    8                    -1
-1                    -1                   -1

Approximates a Laplacian operator, which replaces the central value
with the differential in x and y
Directional kernels
• Vertical edge
-1                     0             1
-1                     0             1
-1                     0             1

Average in vertical direction
Difference in horizontal direction

• Diagonal edge
2                      1             0
1                      0             -1
0                      -1            -2
Complex neighborhood operations
• Median filter: replace central pixel with median
of neighborhood
– Very effective at removing “shot noise”
• Roberts cross: 2 perpendicular directional filter
• Sobel:
– Calculate derivatives in 2 perpendicular directions
– Replace central magnitude with
√ ((δB/ δx)2 + (δB/ δy)2 )
• Kirsch: Apply each of 8 directional filters, and
replace central value with maximum
Complex neighborhood operations
• Olympic filter: in each 5X5 neighborhood. Ignote the
brightest and darkest 4. Replace the central value with
the average of the remaining 17
• Top hat: replace values greater than the average of a
neighborhood by the average for that neigborhood
• Gray scale opening
– First pass: replace central pixel with brightest neighbor
– Second pass: replace pixel with darkest neigbor
– Net effect: dilation of dark features, and erosion of bright
Hybrid – sharpening by difference of
• Apply 2 different size Gaussians to same image
• Subtract smaller from larger Gaussian filtered
result
– Photographically:
• Image in and out of focus
• Invert out-of-focus
• Mat reversed image with in-focus
– Digitally: subtract blurred from unblurred
– http://micro.magnet.fsu.edu/primer/java/digitalimagi
Character recognition
• Instead of regular convolution masks, use
masks that represent characters in the image
• You get a “hit”, or high match, whenever the
mask matches the character!
• However, the characters must ba aligned,
undistorted, etc.
Automatic number plate recognition

Wikipedia: Automatic number plate recognition
Algorithms for ANPR
•   There are six primary algorithms that the software requires for identifying a
licence plate:
•   Plate localisation – responsible for finding and isolating the plate on the picture
•   Plate orientation and sizing – compensates for the skew of the plate and adjusts
the dimensions to the required size
•   Normalisation – adjusts the brightness and contrast of the image
•   Character segmentation – finds the individual characters on the plates
•   Optical character recognition
•   Syntactical/Geometrical analysis – check characters and positions against country
specific rules
•   The complexity of each of these subsections of the program determines the
accuracy of the system. During the third phase (normalisation) some systems use
edge detection techniques to increase the picture difference between the letters
and the plate backing. A median filter may also be used to reduce the visual
"noise" on the image.

-ibid
General object recognition
• How do we recognize specific objects (such as
tanks in aerial images) using machine vision
– Problem of orientation: any orientation may
present itself
• First, scan image with circularly averaged structures
• Then, scan again with specific orietations
• Highly computationally expensive, and not terribly
effective
• We can often do better with Fourier transform
techniques.
Fourier Transform Image Processing
• Any periodic object can be represented by a
summation of a series of cosine waves
• The Operation of Fourier transformation of an
image replaces the image (real space) be a series
of amplitudes and frequencies of the cosine
waves that make it up
• Fourier space is also referred to as frequency
space
• If there are repeats in the stucture at specific
frequencies, these will appear as peaks in Fourier
space
Fourier Transform Image Processing
• High- and low-pass filters
• By enhancing or supressing specific frequencies, we
can enhance or suppress periodic structures within the
image

Molecular Expressions

Java simulation
Examples
• Nuclear pore complex
– Markham rotation
– Fourier transform
• Removal of halftone screen noise
Dangers of Fourier transforms
• Can introduce periodicities where none are
present
• Edge effects

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