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MATHEMATICS OF BINARY MORPHOLOGY and APPLICATIONS IN Vision. The science of form and structure the science of form, that of the outer form, inner structure, and development of living organisms and their parts about changing/counting regions/shapes

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MATHEMATICS OF BINARY MORPHOLOGY

and

APPLICATIONS IN Vision


In general what is morphology

The science of form and structure

the science of form, that of the outer form, inner structure, and development of living organisms and their parts

about changing/counting regions/shapes

Among other applications it is used to pre- or post-process images

via filtering, thinning and pruning

Count regions (granules)

number of black regions

Estimate size of regions

area calculations

In general; what is “Morphology”?

  • Smooth region edges

    • create line drawing of face

  • Force shapes onto region edges

    • curve into a square


What is morphology in computer vision
What is Morphology in computer vision ?

  • Morphology generally concerned with shape and properties of objects.

  • Used for segmentation and feature extraction.

  • Segmentation = used for cleaning binary objects.

  • Two basic operations

    • erosion (opening)

    • dilation (closing)


Morphological operations and algebras
Morphological operations and algebras

  • Different definitions in the textbooks

  • Different implementations in the image processing programs.

  • The original definition, based on set theory, is made by J. Serra in 1982.

  • Defined for binary images - binary operations (boolean, set-theoretical)

  • Can be used on grayscale images - multiple-valued logic operations


Morphological operations on a pc
Morphological operations on a PC

  • Various but slightly different implementations in

  • Scion

  • Paint Shop Pro

  • Adope Photoshop

  • Corel Photopaint

  • mm

Try them, it is a lot of fun


Mathematical morphology set theoretic representation for binary shapes

Mathematical Morphology - Set-theoretic representation for binary shapes


Binary morphology
Binary Morphology

  • Morphological operators are used to prepare binary (thresholded) images for object segmentation/recognition

    • Binary images often suffer from noise (specifically salt-and-pepper noise)

    • Binary regions also suffer from noise (isolated black pixels in a white region). Can also have cracks, picket fence occlusions, etc.

  • Dilation and erosion are two binary morphological operations that can assist with these problems.


Goals of morphological operations
Goals of morphological operations:

  • 1. Simplifies image data

  • 2. Preserves essential shape characteristics

  • 3. Eliminates noise

  • 4. Permits the underlying shape to be identified and optimally reconstructed from their distorted, noisy forms


What is the mathematical morphology
What is the mathematical morphology ?

  • 1. An approach for processing digital image based on its shape

  • 2. A mathematical tool for investigating geometric structure in image

  • The language of morphology is set theory.

    Mathematical morphology is extension to set theory.


Importance of shape in processing and analysis
Importance of Shape in Processing and Analysis

  • Shape is a prime carrier of information in machine vision

  • For instance, the following directly correlate with shape:

    • identification of objects

    • object features

    • assembly defects



Shape operators

Shape Operators

  • Shapes are usually combined by means of :

  • Set Difference based on Set intersection (occluded objects):

Set difference

Set intersection


Morphological operations based on combining base operations
Morphological Operations based on combining base operations

  • The primary morphological operations are dilationanderosion

  • More complicated morphological operators can be designed by means of combining erosions and dilations

We will use combinations of union, complement, intersection, erosion, dilation, translation...

Let us illustrate them and explain how to combine


Libraries of structuring elements
Libraries of Structuring Elements

  • Application specific structuring elements created by the user


Notation

x

-2 -1 0 1 2

-2 -1 0 1 2

B

y

A special set :

the structuring element

Origin at center in this case, but not necessarily centered nor symmetric

X

No necessarily compact

nor filled

3*3 structuring element, see next slide how it works



Explanation of Dilation

Dilation : x = (x1,x2) such that if we center B on them, then the so translated B intersects X.

X

difference

dilation

B


Notation for Dilation

Dilation : x = (x1,x2) such that if we center B on them, then the so translated B intersects X.

How to formulate this definition ?

1) Literal translation

Mathematical definition of dilation

2) Better : from Minkowski’s sum of sets

Another Mathematical definition of dilation uses the concept of Minkowski’s sum

B is ingeneral not the same as B


The Concept

of Minkowski Sum


Minkowski’s Sum

Definition of Minkowski’s sum of sets S and B :

l

Minkowski’s Sum

l


Another View at Dilation

Dilation :

l

Dilation

Dilation


Comparison of Dilation and Minkowski sum

Dilation :

Bx =

x and b are points

Minkowski sum



Dilation and only to top and to rightMinkowski Set

Dilation and Minkowski Set are denoted by + or by 

No unified notation


Dilation is only to top and to rightnot the Minkowski’s sum

Minkowski’s Sum

l


Dilation is only to top and to rightnot the Minkowski’s sum

l

b

b

b

b

Dilation

l

Dilation

l

B is not the same as B




Dilation vs se
Dilation vs only to top and to rightSE

  • Erosion shrinks

  • Dilation expands binary regions

  • Can be used to fill in gaps or cracks in binary images

structuring Element ( SE )

  • If the point at the origin of the structuring element is set in the underlying image, then all the points that are set in the SE are also set in the image

    • Basically, its like OR’ing the SE into the image


Dilation fills holes
Dilation fills holes only to top and to right

  • Fills in holes.

  • Smoothes object boundaries.

  • Adds an extra outer ring of pixels onto object boundary, ie, object becomes slightly larger.


Main applications of dilation
Main Applications of Dilation only to top and to right


Dilation example
Dilation example only to top and to right


Possible problems with morphological operators
Possible problems with Morphological Operators only to top and to right

  • Erosion and dilation clean image but leave objects either smaller or larger than their original size.

  • Opening and closing perform same functions as erosion and dilation but object size remains the same.


More erode and dilate examples
More Erode and Dilate Examples only to top and to right

Input Image

Dilated

Eroded

Made in Paint Shop Pro


Dilation explained pixed by pixel
Dilation explained pixed by pixel only to top and to right

Denotes origin of B i.e. its (0,0)

Denotes origin of A i.e. its (0,0)

B

A


Dilation explained by shape of a
Dilation explained by shape of A only to top and to right

Shape of A repeated without shift

B

Shape of A repeated with shift

A


Properties of dilation

1. fills in valleys between spiky regions only to top and to right

2. increases geometrical area of object

3. sets background pixels adjacent to object's contour to object's value

4. smoothes small negative grey level regions

Properties of Dilation

objects are light (white in binary)

Dilation does the following:


Structuring Element for Dilation only to top and to right

Length 6

Length 5


Structuring Element for Dilation only to top and to right


Structuring Element for Dilation only to top and to right

Single point in Image replaced with this in the Result


Structuring Element for Dilation only to top and to right


Definition of dilation mathematically
Definition of Dilation: Mathematically only to top and to right

  • Dilation is the operation that combines two sets using vector addition of set elements.

  • Let A and B are subsets in 2-D space. A: image undergoing analysis, B: Structuring element, denotes dilation


Dilation versus translation
Dilation versus translation only to top and to right

  • Let A be a Subset of and .

  • The translation of A by x is defined as:

  • The dilation of A by B can be computed as the union of translation of A by the elements of B

x is a vector


Dilation versus translation illustrated
Dilation versus translation, illustrated only to top and to right

Element (0,0)

Shift vector (0,1)

Shift vector (0,0)

B


Dilation using union formula
Dilation using Union Formula only to top and to right

Center of the circle

This circle will create one point

This circle will create no point


Example of dilation with various sizes of structuring elements
Example of Dilation with various sizes of structuring elements

Structuring

Element

Pablo Picasso, Pass with the Cape, 1960


Mathematical properties of dilation
Mathematical Properties of Dilation elements

  • Commutative

  • Associative

  • Extensivity

  • Dilation is increasing

Illustrated in next slide


Illustration of extensitivity of dilation
Illustration of Extensitivity of Dilation elements

A

B

Replaced with

Here 0 does not belong to B and A is not included in A B


More properties of dilation
More Properties of Dilation elements

  • Translation Invariance

  • Linearity

  • Containment

  • Decomposition of structuring element


Dilation
Dilation elements

  • The dilation operator takes two pieces of data as input

    • A binary image, which is to be dilated

    • A structuring element (or kernel), which determines the behavior of the morphological operation

  • Suppose that X is the set of Euclidean coordinates of the input image, and K is the set of coordinates of the structuring element

  • Let Kx denote the translation of K so that its origin is at x.

  • The DILATION of X by K is simply the set of all points x such that the intersection of Kx with X is non-empty


Dilation1

1 elements

1

1

1

1

1

1

1

1

Dilation

Example: Suppose that the structuring element is a 3x3 square with the origin at its center

{ (-1,-1), (0,-1), (1,-1),

(-1,0), (0,0), (1,0),

( 1,1), (0,1), (1,1) }

K =

X =


Dilation2
Dilation elements

Example: Suppose that the structuring element is a 3x3 square with the origin at its center

Note: Foreground pixels are represented by a color and background pixels are empty


Dilation3
Dilation elements

Structuring element

Input

output


Dilation4
Dilation elements

output


Dilation5
Dilation elements

output


Dilation6
Dilation elements


Dilation7
Dilation elements


Erosion elements


Example of Erosion elements

Erosion : x = (x1,x2) such that if we center B on them, then the so translated B is contained in X.

difference

erosion


Notation for Erosion elements

Erosion : x = (x1,x2) such that if we center B on them, then the so translated B is contained in X.

How to formulate this definition ?

1) Literal translation

Erosion

2) Better : from Minkowski’s substraction of sets

Minkowski’s substraction


Notation for Erosion elements

BINARY MORPHOLOGY

Minkowski’s substraction of sets

Erosion



Erosion with other structuring elements elements

Did not belong to X

When the new SE is included in old SE then a larger area is created


Common structuring elements elementsshapes

= origin

x

y

disk

circle

segments 1 pixel wide

Note that here :

points


Problem in BINARY MORPHOLOGY elements

using Minkowski Sum

First we calculate the operation in parentheses to obtain a diamond


PROBLEM BINARY MORPHOLOGY elements

next we calculate the external operation to obtain a hexagon


ANOTHER EXAMPLE OF EROSION elements

Where d is a diameter

Problem :

<d/2

d/2

d


Implementation of dilation: elements

very low computational cost

0

1 (or >0)

Logical or


Implementation of elementserosion:

very low computational cost

0

1

Logical and


More on erosion
More on Erosion elements


Erosion
Erosion elements

  • (Minkowski subtraction)

  • The contraction of a binary region (aka, region shrinking)

  • Use a structuring element on binary image data to produce a new binary image

  • Structuring elements (SE) are simply patterns that are matched in the image

  • It is useful to explain operation of erosion and dilation in different ways.


Typical uses of erosion
Typical Uses of Erosion elements

  • Removes isolated noisy pixels.

  • Smoothes object boundary.

  • Removes the outer layer of object pixels, ie, object becomes slightly smaller.


Properties of erosion

Erosion removes elementsspiky edges

objects are light (white in binary)

decreases geometrical area of object

sets contour pixels of object to background value

smoothes small positive grey level regions

Properties of Erosion:


Erosion example
Erosion Example elements


Erosion explained pixel by pixel
Erosion explained pixel by pixel elements

A

B



How it works
How It Works? elements

  • During erosion, a pixel is turned on at the image pixel under the structuring element origin only when the pixels of the structuring element match the pixels in the image

  • Both ON and OFF pixels should match.

  • This example erodes regions horizontally from the right.








Mathematical definition of erosion
Mathematical Definition of Erosion elements

  • Erosion is the morphological dual to dilation.

  • It combines two sets using the vector subtraction of set elements.

  • Let denotes the erosion of A by B


Erosion in terms of other operations
Erosion in terms of other operations: elements

  • Erosion can also be defined in terms of translation

  • In terms of intersection

Observe that vector here is negative


Reminder this was a

elements

Reminder - this was A


Erosion illustrated in terms of intersection and negative translation
Erosion illustrated in terms of intersection and negative translation

Observe negative translation

Because of negative shift the origin is here


Erosion formula and intuitive example
Erosion formula and intuitive example translation

Center of B is here and adds a point

Center here will not add a point to the Result


Example of Erosions with various sizes of structuring elements

Structuring

Element

Pablo Picasso, Pass with the Cape, 1960


Properties of erosion1
Properties of Erosion elements

  • Erosion is not commutative!

  • Extensivity

  • Erosion is dereasing

  • Chain rule

The chain rule is as sharp operator in Cube Calculus used in logic synthesis. There are more similarities of these algebras


Properties of erosion2
Properties of Erosion elements

  • Translation Invariance

  • Linearity

  • Containment

  • Decomposition of structuring element


Duality relationship between erosion and dilation
Duality Relationship between erosion and dilation elements

  • Dilation and Erosion transformation bear a marked similarity, in that what one does to image foreground and the other does for the image background.

  • , the reflection of B, is defined as

  • Erosion and Dilation Duality Theorem

Observe negative value which is mirror image reflection of B

Similar but not identical to De Morgan rule in Boolean Algebra


Erosion as dual of dilation
Erosion as Dual of Dilation elements

  • Erosion is the dual of dilation

    • i.e. eroding foreground pixels is equivalent to dilating the background pixels.


Duality relationship between erosion and dilation1

Easily visualized on binary image elements

Template created with known origin

Template stepped over entire image

similar to correlation

Dilation

if origin == 1 -> template unioned

resultant image is large than original

Erosion

only if whole template matches image

origin = 1, result is smaller than original

Duality Relationship between erosion and dilation

Another look at duality


One more view at erosion with examples
One more view at Erosion with examples elements

  • To compute the erosion of a binary input image by the structuring element

  • For each foreground pixel superimpose the structuring element

  • If for every pixel in the structuring element, the corresponding pixel in the image underneath is a foreground pixel, then the input pixel is left as it is

  • Otherwise, if any of the corresponding pixels in the image are background, however, the input pixel is set to background value


Erosion1
Erosion elements


Erosion example with dilation and negation
Erosion example with dilation and negation elements

We want to calculate this

We dilate with negation


Erosion2
Erosion elements

.. And we negate the result

We obtain the same thing as from definition


Morphological operations in terms of more general neighborhoods
Morphological Operations in terms of more general neighborhoods

This exists in Matlab


Erode and dilate in terms of more general neighborhoods
Erode and Dilate in terms of more general neighborhoods neighborhoods

Yet another loook at Duality Relationship between erosion and dilation



Boundary extraction
Boundary Extraction neighborhoods


Erode and binary contour in matlab
Erode and Binary Contour in Matlab neighborhoods

Erosion can be used to find contour

Dilation can be also used for it - think how?


Edge detection
Edge detection neighborhoods

This subtraction is set theoretical

Dilate - original

Now you need to invert the image

There are more methods for edge detection


Opening closing
Opening & Closing neighborhoods

  • Opening and Closing are two important operators from mathematical morphology

  • They are both derived from the fundamental operations of erosion and dilation

  • They are normally applied to binary images


Open and close

Close = Dilate neighborhoodsnext Erode

Open = Erode next Dilate

Open and Close

Original image

eroded

dilated

dilated

eroded

Open

Close


OPENING neighborhoods


OPENING neighborhoods

Opening :

also

OPENING

difference

  • Supresses :

    • small islands

    • ithsmus (narrow unions)

    • narrow caps



Open neighborhoods

  • An erosion followed by a dilation

  • It serves to eliminate noise

  • Does not significantly change an object’s size


Comparison of opening and erosion
Comparison of Opening and Erosion neighborhoods

  • Opening is defined as an erosion followed by a dilation using the same structuring element

  • The basic effect of an opening is similar to erosion

  • Tends to remove some of the foreground pixels from the edges of regions of foreground pixels

  • Less destructive than erosion

  • The exact operation is determined by a structuring element.


Opening example
Opening Example neighborhoods

  • What combination of erosion and dilation gives:

    • cleaned binary image

    • object is the same size as in original

Original


Opening example cont
Opening Example Cont neighborhoods

  • Erode original image.

  • Dilate eroded image.

  • Smooths object boundaries, eliminates noise (isolated pixels) and maintains object size.

Original

Erode

Dilate


One more example of opening
One more example of Opening neighborhoods

  • Erosion can be used to eliminate small clumps of undesirable foreground pixels, e.g. “salt noise”

  • However, it affects all regions of foreground pixels indiscriminately

  • Opening gets around this by performing both an erosion and a dilation on the image


CLOSING neighborhoods


EXAMPLE OF CLOSING neighborhoods

Closing :

also

  • Supresses :

    • small lakes (holes)

    • channels (narrow separations)

    • narrow bays


BINARY MORPHOLOGY neighborhoods

Closing previous image with other structuring elements

With bigger rectangle like this

With smaller cross like this


Application : shape smoothing and noise filtering neighborhoods

Papilary lines recognition


disk radius 6 neighborhoods

original

negated

threshold

closing

opening

and withthreshold

Application :

segmentation of microstructures (Matlab Help)


PROPERTIES IN BINARY MORPHOLOGY neighborhoods

  • Properties

    • all of them are increasing :

    • opening and closing are idempotent :


EXTENSIVE VERSUS ANTI-EXTENSIVE OPERATIONS neighborhoods

  • dilation and closing are extensive operations

  • erosion and opening are anti-extensive operations:


DUALITIES OF MORPHOLOGICAL OPERATORS neighborhoods

  • dualityof erosion-dilation, opening-closing,...


operations with big structuring elements can be done

by a succession of operations with small s.e’s


HIT-OR-MISS neighborhoods

Hit-or-miss :

Bi-phase structuring element

“Hit” part

(white)

“Miss” part

(black)


HIT or MISS FOR ISOLATED POINTS neighborhoods

Looks for pixel configurations :

background

foreground

doesn’t matter


ISOLATED POINTS neighborhoods

isolated points at4 connectivity



Close
Close neighborhoods

  • Dilation followed by erosion

  • Serves to close up cracks in objects and holes due to pepper noise

  • Does not significantly change object size


More examples of closing
More examples of Closing neighborhoods

  • What combination of erosion and dilation gives:

    • cleaned binary image

    • object is the same size as in original

Original


More examples of closing cont
More examples of Closing cont neighborhoods

  • Dilate original image.

  • Erode dilated image.

  • Smooths object boundaries, eliminates noise (holes) and maintains object size.

Dilate

Erode

Original


Closing as dual to opening
Closing as dual to Opening neighborhoods

  • Closing, like its dual operator opening, is derived from the fundamental operations of erosion and dilation.

  • Normally applied to binary images

  • Tends to enlarge the boundaries of foreground regions

  • Less destructive of the original boundary shape

  • The exact operation is determined by a structuring element.


Closing is opening in revers
Closing is opening in revers neighborhoods

  • Closing is opening performed in reverse.

  • It is defined simply as a dilation followed by an erosion using the same



Mathematical definitions of opening and closing
Mathematical Definitions of Opening and Closing neighborhoods

  • Opening and closing are iteratively applied dilation and erosion

    Opening

    Closing


Relation of opening and closing
Relation of Opening and Closing neighborhoods

Difference is only in corners


Opening and closing are idempotent
Opening and Closing are neighborhoodsidempotent

  • Their reapplication has not further effects to the previously transformed result


Properties of opening and closing
Properties of Opening and Closing neighborhoods

  • Translation invariance

  • Antiextensivity of opening

  • Extensivity of closing

  • Duality


Example of Openings with various sizes of structuring elements

Structuring

Element

Pablo Picasso, Pass with the Cape, 1960


Example of Closings with various sizes of structuring elements

Structuring

Element

Example of Closing


Thinning elements

and Thickening


Thinning and Thickening elements

Thinning :

Thickenning :

  • Depending on the structuring elements (actually, series

  • of them), very different results can be achieved :

    • Prunning

    • Skeletons

    • Zone of influence

    • Convex hull

    • ...


Prunning at 4 connectivity elements

Prunning at 4 connectivity : remove end points by a sequence of thinnings

This point is removed with dark green neighbors

1 iteration =


IDEMPOTENCE shown as a result of thinning elements

1st iteration

2nd iteration

3rd iteration: idempotence


Other thinning operations elements

background

doesn’t matter

foreground


USING EROSION TO FIND CONTOURS elements

Contours of binary regions :

erosion

difference

Contour found with larger mask


CONTOURS with different connectivity patterns elements

8-connectivity

contour

4-connectivity

contour

4-connectivity

8-connectivity

Important for perimeter computation.


Use of thickening: elementsConvex hull

ii. Convex hull : union of thickenings, each up to idempotence

Original shaper

Thickening with first mask

Union of four thickenings



iii. Skeleton : elements

Maximal disk : disk centered at x, Dx, such that

DxX and no other Dy contains it .

Skeleton : union of centers of maximal disks.


PROBLEMS with skeletons elements

  • Problems :

  • Instability : infinitessimal variations in the border of X

  • cause large deviations of the skeleton

  • not necessarily connex even though X connex

  • good approximations provided by thinning with

  • special series of structuring elements



Example of iterative thinning with 8 masks elements

result of

1st iteration

2nd iteration reaches

idempotence


Thinning elements

with thickening

20 iterations of thinning color white

40 iterations

Thickening

color white

Some sort of region clustering


Skeletonization for OCR elements

BINARY MORPHOLOGY

Application : skeletonization for OCR by graph matching

skeletonization

vectorization


Hit-or-Miss elements

and 3 rotations

skeletonization

Application : skeletonization for OCR by graph matching


Calculation of Geodesic zones of influence (GZI) elements

X set of n connex components {Xi},i=1..n.

The zone of influence of Xi , Z(Xi) ,is the set of points closer to some point of Xi than to a point of any other component.

Also, Voronoi partition.

Dual to skeleton.


Calculating and using Geodesic Zones of Influence elements

thr

erosion

7x7

GZI

and

opening

5x5




Example morphological processing of handwritten digits
Example – Morphological Processing of Handwritten Digits elements

thresholding

thinning

smoothing

opening


PROGRAMMING elements

OF MORPHOLOGICAL OPERATIONS


USING LISP elements


USING LISP elements


USING LISP elements


USING LISP elements


USING LISP elements



Morphological filtering1
Morphological Filtering elements

  • Main idea of Morphological Filtering:

    • Examine the geometrical structure of an image by matching it with small patterns called structuring elements at various locations

    • By varying the size and shapeof the matching patterns, we can extract useful informationabout the shape of the different parts of the imageand their interrelations.

    • Combine set-theoretical and morphological operations:


Example 1 morphological filtering
Example 1: elements Morphological filtering

  • Noisy image will break down OCR systems

Noisy image

Clean original image


Morphological filtering mf

By applying MF, we increase the OCR accuracy! elements

Morphological filtering (MF)

Restored image


Rank filter median
Rank Filter Median elements

Input

1 operation

2 operations


Postprocessing
Postprocessing elements

  • Opening followed by closing.

  • Removes noise and smoothes boundaries.


Postprocessing1
Postprocessing elements

  • Opening followed by closing.

  • Removes noise and smoothes boundaries.



erosion elements

dilation



Removal of border objects
Removal of Border Objects elements

Marker is the border itself


Summary on morphological approaches
Summary on Morphological Approaches elements

  • Mathematical morphology is an approach for processing digital image based on its shape

  • The language of morphology is set theory

  • The basic morphological operations are erosion and dilation

  • Morphological filtering can be developed to extract useful shape information

  • Methods can be extended to more values and more dimensions

  • Nice mathematics can be formulated - non-linear


Conclusion
Conclusion elements

  • Segmentation separates an image into regions.

  • Use of histograms for brightness based segmentation.

    • Peak corresponds to object.

    • Height of peak corresponds to size of object.

  • If global image histogram is multimodal, local image region histogram may be bimodal.

  • Local thresholds can give better segmentation.


Conclusion1
Conclusion elements

  • Postprocessing uses morphological operators.

  • Same as convolution only use Boolean operators instead of multiply and add.

    • Erosion clears noise, makes smaller.

    • Dilation fills in holes, makes larger.

  • Postprocessing

    • Opening and closing to clean binary images.

    • Repeated erosion with special rule produces skeleton.


Problems 1 6
Problems 1 - 6 elements

  • 1. Write LISP or C++ program for dilation of binary images

  • 2. Modify it to do erosions (few types)

  • 3. Modify it to perform shift and exor operation and shift and min operation

  • 4. Generalize to multi-valued algebra

  • 5. Create a comprehensive theory of multi-valued morphological algebra and its algorithms (publishable).

  • 6. Write a program for inspection of Printed Circuit Boards using morphological algebra.


Problem 7
Problem 7. elements

  • Electric Outlet Extraction has been done using a combination of Canny Edge Detection and Hough Transforms

  • Write a LISP program that will use only basic morphological methods for this application.


Image processing for electric outlet how
Image Processing for electric outlet, how? elements

Alpha filtering

DPC compression

Perimeter

Fractal

Gaussian Filter

Band Pass Filter

Homomorphic Filtering

Contrast

Sharper

Least Square Restoration

Warping

Dilation

  • Currently there are many, many ways to approach this problem

    • Segmentation

    • Edge Detection

    • DPC compression

    • FFT

    • IFFT

    • DFT

    • Thinning

    • Growing

    • Haar Transform

    • Hex Rotate


Image processing how
Image Processing, how? elements

  • Create morphological equivalents of other image processing methods.

  • New, publishable, use outlet problem as example to illustrate


Problem 8 openings and closings as examples
Problem 8. Openings and Closings as examples. elements

  • The solution here is to follow up one operation with the other.

  • An opening is defined as an erosion operation followed by dilation using the same structuring element.

  • Similarly, a closing is dilation followed by erosion.

  • Define and implement other combined operations.


Problems 9 12
Problems 9 - 12. elements

  • 9. Generalize binary morphological algebra from 2 dimensional to 3 dimensional images. What are the applications.

  • 10. Write software for 9.

  • 11. Generalize your generalized multi-valued morphological algebra to 3 or more dimensions, theoretically, find properties and theorems like those from this lecture.

  • 12. Write software for 11.


Problem 13
Problem 13 elements

  • Mathematical morphology uses the concept of structuring elements to analyze image features.

  • A structuring element is a set of pixels in some arrangement that can extract shape information from an image.

  • Typical structuring elements include rectangles, lines, and circles.

  • Think about other structuring elements and their applications.


Morphological operations matlab
Morphological Operations: Matlab elements

BWMORPH Perform morphological operations on binary image.

BW2 = BWMORPH(BW1,OPERATION) applies a specific morphological operation to the binary image BW1.

BW2 = BWMORPH(BW1,OPERATION,N) applies the operation N times. N can be Inf, in which case the operation is repeated until the image no longer changes.

OPERATION is a string that can have one of these values:

'close' Perform binary closure (dilation followed by erosion)

'dilate' Perform dilation using the structuring elementones(3)

'erode' Perform erosion using the structuring elementones(3)

'fill' Fill isolated interior pixels (0's surrounded by1's)

'open' Perform binary opening (erosion followed bydilation)

'skel' With N = Inf, remove pixels on the boundariesof objects without allowing objects to break apart

  • demos/demo9morph/


Sources
Sources elements

D.A. Forsyth, University of New Mexico,

Qigong Zheng, Language and Media Processing Lab

Center for Automation Research

University of Maryland College Park

October 31, 2000

John Miller

Matt Roach

J. W. V. Miller and K. D. Whitehead

The University of Michigan-Dearborn

Spencer Lustor

Light Works Inc. C. Rössl, L. Kobbelt, H.-P. Seidel, Max-Planck Institute for, Computer Science, Saarbrücken, Germany

LBA-PC4

Howard Schultz

Shreekanth Mandayam

ECE Department

Rowan University

D.A. Forsyth, University of New Mexico


More recent Sources elements

  • Howie Choset

  • G.D. Hager,

  • Z. Dodds,

  • Dinesh Mocha


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