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IMAGE PROCESSING
Bhupendra P.Karandikar
Dept.of Instrumentation Science
University of Pune
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INTRODUCTION Image:An image is defined in real world as afunction of two variables, a(x,y)
Where, a = amplitude(brightness)
x,y= co-ordinate position
An image frequently contains collections ofsub-images called as regions-of-interest(ROIs).
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Image operations
Digital technology has made it possible tomanipulate multi-dimensional signals withcomputer systems that can be divided into:
Image Analysis
Image Transformation
Image understanding
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Image operations
Image analysis
Input : Image
Output : Measurement results
Process: Comparison in under test sample with standard
gauge or best fit circle.
Example: a online inspection Detection & measurement ofdiameter of a rubber O rings manufactured.
Advantages:
Non-contact measurements.
Human being not involved.
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Image operations
Image Transformation
Input : Image
Output : Image
Process: Image morphing comparing each pixel of the
image.
Example: Digital image mixing, animation tool.
Advantages:
No series of picture frames are required.
any type of image can be produced.
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Image operations
Image Understanding
Input : Image
Output : High level description
Process: Collecting & arranging the Image information
which can be compared with another image.
Example: Finger print matching, Human Face recognition
Optical character recognition.
Advantages:
Machine generated support to results by human being.
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Objective of the seminar
Featuring the mathematical operations
used for these three operations to the
image
Image
Grabbing
tool
Image
Processing
tool/s
Operated
Image/
image
information
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Digital images
Image representation:
f(x,y) with origin as the upper left corner.
Sampling :
- measuring value of image at discrete intervals in space.- sampling rate
- spatial resolution
Quantization:- Grey scale image
- Colour image, f(x,y) is a vector with three components R,G,B. This
can be represented in RGB Colour cube.
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Grey scale image
Human tendency to average brightness over
small areas, so that black dots and their white
background merge and perceived as grey
shade. Use of this in black & white printing technology
Half toning:
The process of generating a binary pattern of
black and white dots from an image.
Patterning Dithering
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Digital Half toning technique
Patterning:
- replacing each pixel by a binary font
- each grey level is assigned a value ofbinary font
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Dithering
thresholding the image against a dithermatrix.
Each pixel is compared with dither matrix
The pixel becomes white if value exceedsthe threshold or black otherwise.
0 128
D1=example for operations on 8 bit
192 64 images
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The Colour cube
RED
GREEN
CYAN
MAGENTA
BLUE
WHITE
BLACK
YELLOW1
0
1
1
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Simple image operations
I. Addition & averaging:
- addition of two images pixel by pixel
- two images of identical dimensions- Noise removal by averaging no.of images
- alpha blending, g(x,y) = f1(x,y)+(1- ) f2(x,y)
- =0.5 gives evenly weighted average
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Simple image operations
Examples:
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Simple image operations
Subtraction:- subtraction of two images
- change detection in two images
- g(x,y) = I f1(x,y) - f2(x,y)I
Division:- division of two images for meaningful results
- ratio image can be formed featuring illumination &
surface topography- remote sensing applications
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Simple image operations
Adjustments of brightness & contrast:
- converting an image into interpretable form
- image brightness, g(x,y) = f(x,y) + b
- image contrast, g(x,y) = a f(x,y)
where, [f1,f2] are grey levels
g is the range(0-255).
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Simple image operationsLinear mapping
Map a particular range of grey levels [f1,f2] onto
a new range [g1,g2].
g(x,y) = g1 +(g2-g1 / f2-f1)[f(x,y)]
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Simple image operationsLinear mapping
f1 , here acts as a threshold up to and including
which grey levels are mapped as 0 and values
higher are mapped onto 255, hence this technique
is called as Thresholding.
This technique is used for image enhancement.
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Simple image operationsNon-linear mapping
Disadvantage of linear mapping: single valued
function is necessary.
Logarithmic mapping solves this problem, by
logarithmic mapping of input grey level onto outputgrey level.
Effective to enhance detail in the darker region of
image
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Simple image operationsNon-linear mapping
Example:
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Image histograms
The histogram of an image records the
frequency distribution of grey levels in that
image.
In bin 0, we record the number of times a greylevel of 0 occurs & similarly for other grey levels.
Useful indication of relative importance of
different grey levels in the image and judging the
requirements of contrast & brightness
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Histogram equalization
Redistributes grey levels in an attempt to flatten
the frequency distribution
More grey levels are allocated where there are
most pixels, fewer grey levels where there are
fewer pixels.
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Presentation for Seminar 3
Morphological Image processing
- Basic Concepts
- Fundamental operations- Compound operations
- Filtering
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Review of 2nd seminar
Digital images
Image operations
- Half toning, Dithering, addition,subtraction, division
- Linear and non-linear mapping
- Histogram and histogram equalization
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Histogram equalization
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Morphological operationsIntroduction
Describes range of non-linear image
processing techniques dealing with shape
of image
Removal of imperfections caused by
noise, texture or inaccurate thresholding to
binary images
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Morphological operationsFundamental binary operations
Basic concept:
typically to probe an image with a small
shape or template known as structuringelement.
The structuring element is positioned at
various positions in the image and compared
with the corresponding neighborhood pixels.
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Morphological operationsFundamental binary operations
Structuring elements
A matrix of known dimension and with pre-
defined elements.
Types of structuring elements:
- Square shaped
- Diamond shaped
- Cross shaped etc.
Advantage & disadvantages of structuring
elements- for erosion / dilation.
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Morphological operationsStructuring elements
Examples:
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Morphological operationsFundamental binary operations
Fitting & hitting:
Checking whether the element hits the
image or fits the image.
Hitting of an image:
Fitting of an image:
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Morphological operationsFundamental binary operations
Erosion:
Pixels are eroded from both the inner and outer
boundaries of regions, so the erosion will enlarge
the holes enclosed by a single region as well asmaking the gap between different regions larger.
This is fitting of image.
Erosion of an image f by a structuring element s
is given by,f s
g (x,y) = 1 if s fits f
= 0 otherwise.
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Morphological operationsErosion operations
Example of erosion:
Advantages:
Removal of unwanted, small scale features.
Disadvantage:
Side-effect on image as reduction in size of features
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Boundary detection:
Eroded image lacks boundary pixels, hence we
can subtract the eroded image from the original
image to get the boundary.
g = f - (f s)
Morphological operationsApplications of erosion
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Morphological operationsApplications of erosion
Example: Boundary detection
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Morphological operationsDilation operation
Dilation:
the structuring element hits the boundary regionand source image is enlarged, known as hitting
to image.
Dilation of an image f by a structuring element s is given by,
f sg (x,y) = 1 if s hits f
= 0 otherwise.
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Morphological operationsDilation operation
Dilation operation will shrink the holes
enclosed by a single region and make the
gaps between different regions smaller.
To fill in any small intrusions into a regions
boundaries.
Opposite effect of erosion.
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Morphological operationsApplications of erosion
Disadvantage:
Boundary finding is highly dependent on the ratio
of structuring element to image resolution.
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Morphological operationsApplication of Dilation
Enlarging or enhancing the regions of
interest (ROI)
Smoothening of the boundary regions
Limitations:
Exhibits unnecessary alteration to thesharp edges
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Morphological operationsDilation operations
Example:
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Morphological operationsCompound operations
Combinations of erosion, dilation & various
other operations
- Inversion- opening of an image
- Closing of an image
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Review of 3rd seminar
Structuring elements & its types
Erosion
Applications of erosion Dilation
Applications of dilation
Introduction of Opening & closing of image
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Morphological filtering
Filtering is removal of noise andunnecessary information from image.
Example: a biscuit inspection system.
Objective is to measure / monitor size &shape.
The noise is the image capturing elementnoise or the biscuit crumbs.
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Morphological filtering
Example:
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Segmentation
Partitioning of the image into distinct regions
Applications as Image analysis & interpretation- Industrial inspection
- tracking of objects
- detection and measurement of objects
Low level Image processing
High level Image processing
Segmentation
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Segmentation
Classification:
- Non-contextual: these techniques ignore
the relationships between the two featuresof an image
- Contextual: exploit the relationships
between the two features in an image
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SegmentationNon-contextual techniques
Regions are treated as independentimages
Thresholding: Classification of pixels in
two distinct categories. The strength of an edge-typically grey
level is detected by assigning value 0 if
the gradient falls below the threshold or anon-zero (usually 1) is assigned.
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Non-contextual techniquesThresholding
Classification of pixels into two categories.
0 f(x,y) < T
g(x,y) =
1 , f(x,y) T.
For the brighter feature.
This shows that this technique is featuredependent and cannot be used for automaticdetection.
Example, a robot camera to view its cards in hand.
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Example:
Non-contextual techniquesThresholding
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Thresholding by a pair:
0 f(x,y) < T1g(x,y) = 1 T1 f(x,y) T2
0 f(x,y) > T2.
Non-contextual techniquesThresholding
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Thresholding groups together according to
their global attribute, such as grey level.
This can be more successful as it takes into
account that pixels belonging to a single
object are close to one another.
It can be based on concept of similarity or
concept of discontinuity.
Contextual techniques
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This is a central concept of both edge and
region based approaches.
Types: 4-neighbourhood8-neighbourhood
Contextual techniquesPixel connectivity
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Convolution
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