Mathematical Morphology-Colors Pictures And Digital Image Processing-Lecture Slides, Slides of Digital Image Processing

Dr. Chittaranjan Verma delivered this lecture for Digital Image Processing course at B R Ambedkar National Institute of Technology. It includes: Mathematical, Morphology, Colors, Pictures, Digital, Image, Processing, Mathematical

Typology: Slides

2011/2012

Uploaded on 07/20/2012

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Definitions
Morphology
A branch of biology which deals with the form and
structure of animals and plants
Mathematical Morphology
A tool for extracting image components that are useful in
the representation and description of region shapes
The language of mathematical morphology is Set Theory
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Download Mathematical Morphology-Colors Pictures And Digital Image Processing-Lecture Slides and more Slides Digital Image Processing in PDF only on Docsity!

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Definitions ^ MorphologyA branch of biology which deals with the form andstructure of animals and plants ^ Mathematical Morphology^ ^ A tool for extracting image components that are useful inthe representation and description of region shapes^ ^ The language of mathematical morphology is Set Theory

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Set theory ^ The set space of binary image is Z

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i.e. each element of the set is a 2D vector whosecoordinates are the (x,y) of a black (or white,depending on the convention) pixel in the image  The set space of gray level image is Z

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i.e. each element of the set is a 3D vector: (x,y) andintensity level. NOTE:Set Theory and Logical operations are covered in:Section 9.1, Chapter # 9, 2

nd^ Edition DIP by Gonzalez Section 2.6.4, Chapter # 2, 3

rd^ Edition DIP by Gonzalez

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Set theory ^ Subset: if every element of A is also an element ofanother set B, the A is said to be a subset of B ^ Union: The set of all elements belonging either to A,B or both ^ Intersection: The set of all elements belonging toboth A and B

A^ BC A^ B ^  D A^

B ^ 

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Set theory ^ Two sets A and B are said to be disjoint or mutuallyexclusive if they have no common element ^ Complement: The set of elements not contained inA ^ Difference of two sets A and B, denoted by A – B, isdefined asi.e. the set of elements that belong to A, but not to B

A^ B^ 

 { | } c A w w

A ^

 {^ |^

,^

}^

c

A^ B^

w w^

A w^

B^ A

B

^ ^

^

^ 

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Set theory ^ Reflection of set Bi.e. the set of element w, such that w isformed by multiplying each of twocoordinates of all the elements of set B by -1 ^ Translation of set A by point z = (z

,z^ ), denoted 1 2

^ {^ (A), is defined asz^

|^

,^ for^

}

B^ w w

b^

b^ B ^

 ^

(^ )^

{^ |^

,^ for^

}

A^ w wz

a^ z

a

A

^

^ ^

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Logical operations

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Dilation ^ Definition 1: The dilation of two sets A and B isdefined as:i.e. when the reflection of set B about its origin is shiftedby z, the dilation of A by B is the set of all displacementssuch that

overlaps A by at least one element

^ Definition 2: Minkowski’s Addition DefinitionIt states that the dilation of A by B is obtained by the union ofall translates of A, with the translation distance equal to therow and column index of pixels of B that are logical 1

{ | ( )^

z

A^ B^

z^ B^

A

^ ^

 B

(^ ) b b B

A^ B^

A 

^ ^ 

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Dilation ^ Set B is commonly referred to as the structuring elementin dilation as well as in many morphological operations ^ Effects^ ^ Expands the size of 1-valued objects^ ^ Smoothes object boundaries^ ^ Closes holes and gaps

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Dilation ^ Dilation process is analogous to convolution process^ ^ B (structuring element) is considered as convolutionmask^ ^ B is flipped around the origin and then successivelydisplacing it so that it slides over set (image) A^ ^ But, Dilation is based on set operations whereasconvolution is based on arithmetic operations ^ Rule for Dilation^ In a binary image, if any of the pixel (in the neighborhooddefined by structuring element) is 1, then output is 1

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Dilation: Example^1

0 0

1 1

1 0

0

0 1

0 1

1

1 0

1 1

1 0

0 0

1

0 0

1 1

1

^

1 0

0

0 1

1 0

0

0 1

0 0

1

1 0

0 1

1 0

0 0

1

0 0

0 1

1

^

^

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Dilation: Example

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Dilation: Example

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Erosion ^ Effects^ ^ Shrinks the size of 1-valued objects^ ^ Smoothes object boundaries^ ^ Removes small objects ^ Rule for Erosion^ In a binary image, if any of the pixel (in the neighborhooddefined by structuring element) is 1, then output is 1

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Erosion: Example^1

1 1

1 1

1 1

1

1 0

1 1 1

1 1

0 0

0 0

1 0

0

1 0

0 0

0

 