SlideShare a Scribd company logo
1 of 67
Prepared by
K.Indragandhi,AP(Sr.Gr.)/ECE
Basic Relationships between Pixels
f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - -
f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - -
f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - -
f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - -
I I I I I - - - - -
I I I I I - - - - -
 A Pixel p at coordinates ( x, y) has 4 horizontal and vertical neighbors.
 Their coordinates are given by:
(x+1, y) (x-1, y) (x, y+1) &
(x, y-1)
f(2,1) f(0,1) f(1,2)
f(1,0)
 This set of pixels is called the 4-neighbors of p denoted by N4(p).
 Each pixel is unit distance from ( x ,y).
f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - -
f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - -
f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - -
f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - -
I I I I I - - - - -
I I I I I - - - - -
 A Pixel p at coordinates ( x, y) has 4 diagonal neighbors.
 Their coordinates are given by:
(x+1, y+1) (x+1, y-1) (x-1, y+1) &
(x-1, y-1)
f(2,2) f(2,0) f(0,2)
f(0,0)
 This set of pixels is called the diagonal-neighbors of p denoted by
ND(p).
 diagonal neighbors + 4-neighbors = 8-neighbors of p.
f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - -
f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - -
f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - -
f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - -
I I I I I - - - - -
I I I I I - - - - -
Adjacency: Two pixels are adjacent if they are neighbors and
their intensity level ‘V’ satisfy some specific criteria of
similarity.
e.g. V = {1}
V = { 0, 2}
Binary image = { 0, 1}
Gray scale image = { 0, 1, 2, ------, 255}
In binary images, 2 pixels are adjacent if they are neighbors &
have some intensity values either 0 or 1.
In gray scale, image contains more gray level values in range 0
to 255.
4-adjacency: Two pixels p and q with the values from set ‘V’
are 4-adjacent if q is in the set of N4(p).
e.g. V = { 0, 1}
1 1 2
1 1 0
1 0 1
p in RED color
q can be any value in GREEN color.
8-adjacency: Two pixels p and q with the values from set ‘V’
are 8-adjacent if q is in the set of N8(p).
e.g. V = { 1, 2}
0 1 1
0 2 0
0 0 1
p in RED color
q can be any value in GREEN color
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p) OR
(ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels
whose values are from ‘V’.
e.g. V = { 1 }
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 i
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p)
e.g. V = { 1 }
(i) b & c
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p)
e.g. V = { 1 }
(i) b & c
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
Soln: b & c are m-adjacent.
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p)
e.g. V = { 1 }
(ii) b & e
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p)
e.g. V = { 1 }
(ii) b & e
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
Soln: b & e are m-adjacent.
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p) OR
e.g. V = { 1 }
(iii) e & i
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 i
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in ND(p) & the set N4(p) n N4(q) have no pixels
whose values are from ‘V’.
e.g. V = { 1 }
(iii) e & i
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in ND(p) & the set N4(p) n N4(q) have no pixels
whose values are from ‘V’.
e.g. V = { 1 }
(iii) e & i
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
Soln: e & i are m-adjacent.
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p) OR
(ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels
whose values are from ‘V’.
e.g. V = { 1 }
(iv) e & c
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
m-adjacency: Two pixels p and q with the values from set ‘V’
are m-adjacent if
(i) q is in N4(p) OR
(ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels
whose values are from ‘V’.
e.g. V = { 1 }
(iv) e & c
0 a 1 b 1 c
0 d 1 e 0 f
0 g 0 h 1 I
Soln: e & c are NOT m-adjacent.
Connectivity: 2 pixels are said to be connected if their exists a path
between them.
Let ‘S’ represent subset of pixels in an image.
Two pixels p & q are said to be connected in ‘S’ if their exists a path
between them consisting entirely of pixels in ‘S’.
For any pixel p in S, the set of pixels that are connected to it in S is
called a connected component of S.
Paths: A path from pixel p with coordinate ( x, y)
with pixel q with coordinate ( s, t) is a sequence of
distinct sequence with coordinates (x0, y0), (x1, y1),
….., (xn, yn) where
(x, y) = (x0, y0)
& (s, t) = (xn, yn)
Closed path: (x0, y0) = (xn, yn)
Example # 1: Consider the image segment shown in figure.
Compute length of the shortest-4, shortest-8 & shortest-m paths
between pixels p & q where,
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-4 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
So, Path does not exist.
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-8 path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
So, shortest-8 path = 4
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
Example # 1:
Shortest-m path:
V = {1, 2}.
4 2 3 2 q
3 3 1 3
2 3 2 2
p 2 1 2 3
So, shortest-m path = 5
Region: Let R be a subset of pixels in an image. Two regions Ri
and Rj are said to be adjacent if their union form a connected set.
Regions that are not adjacent are said to be disjoint.
We consider 4- and 8- adjacency when referring to regions.
Below regions are adjacent only if 8-adjacency is used.
1 1 1
1 0 1 Ri
0 1 0
0 0 1
1 1 1 Rj
1 1 1
Boundaries (border or contour): The boundary of a region
R is the set of points that are adjacent to points in the
compliment of R.
0 0 0 0 0
0 1 1 0 0
0 1 1 0 0
0 1 1 1 0
0 1 1 1 0
0 0 0 0 0
RED colored 1 is NOT a member of border if 4-connectivity is
used between region and background. It is if 8-connectivity is
used.
(1=2)
(3=4)
(1=5)
Distance Measures: Distance between pixels p, q & z with
co-ordinates ( x, y), ( s, t) & ( v, w) resp. is given by:
a) D( p, q) ≥ 0 [ D( p, q) = 0 if p = q] …………..called
reflexivity
b) D( p, q) = D( q, p) .………….called
symmetry
c) D( p, z) ≤ D( p, q) + D( q, z) ..………….called
transmitivity
Euclidean distance between p & q is defined as-
De( p, q) = [( x- s)2 + (y - t)2]1/2
City Block Distance: The D4 distance between p & q is
defined as
D4( p, q) = |x - s| + |y - t|
In this case, pixels having D4 distance from ( x, y) less than or
equal to some value r form a diamond centered at ( x, y).
2
2 1 2
2 1 0 1 2
2 1 2
2
Pixels with D4 distance ≤ 2 forms the following contour of
constant distance.
Chess-Board Distance: The D8 distance between p & q is
defined as
D8( p, q) = max( |x - s| , |y - t| )
In this case, pixels having D8 distance from ( x, y) less than or
equal to some value r form a square centered at ( x, y).
2 2 2 2 2
2 1 1 1 2
2 1 0 1 2
2 1 1 1 2
2 2 2 2 2
Pixels with D8 distance ≤ 2 forms the following contour of
constant distance.
 The AND operator is usually used to mask out
part of an image.
 Parts of another image can be added with a
logical OR operator.
Result of AND Result of OR
OR
pixelrelationships-m-1.pptx.ppt

More Related Content

Similar to pixelrelationships-m-1.pptx.ppt

chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdf
chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdfchapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdf
chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdfSomeone413715
 
Chapter 2 Image Processing: Pixel Relation
Chapter 2 Image Processing: Pixel RelationChapter 2 Image Processing: Pixel Relation
Chapter 2 Image Processing: Pixel RelationVarun Ojha
 
On the Odd Gracefulness of Cyclic Snakes With Pendant Edges
On the Odd Gracefulness of Cyclic Snakes With Pendant EdgesOn the Odd Gracefulness of Cyclic Snakes With Pendant Edges
On the Odd Gracefulness of Cyclic Snakes With Pendant EdgesGiselleginaGloria
 
E field dipole
E field dipoleE field dipole
E field dipoleFFMdeMul
 
On the Zeros of Polar Derivatives
On the Zeros of Polar DerivativesOn the Zeros of Polar Derivatives
On the Zeros of Polar Derivativespaperpublications3
 
Geometry (Grid & section formula)
Geometry (Grid & section formula)Geometry (Grid & section formula)
Geometry (Grid & section formula)itutor
 
planes and distances
planes and distancesplanes and distances
planes and distancesElias Dinsa
 
THREE DIMENSIONAL GEOMETRY
THREE DIMENSIONAL GEOMETRYTHREE DIMENSIONAL GEOMETRY
THREE DIMENSIONAL GEOMETRYUrmila Bhardwaj
 
Three dim. geometry
Three dim. geometryThree dim. geometry
Three dim. geometryindu thakur
 
Lines and curves algorithms
Lines and curves algorithmsLines and curves algorithms
Lines and curves algorithmsMohammad Sadiq
 
Pixel Relationships Examples
Pixel Relationships ExamplesPixel Relationships Examples
Pixel Relationships ExamplesMarwa Ahmeid
 
Calculus a Functions of Several Variables
Calculus a Functions of Several Variables Calculus a Functions of Several Variables
Calculus a Functions of Several Variables Harington Dinklage
 
Further pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsFurther pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsDennis Almeida
 

Similar to pixelrelationships-m-1.pptx.ppt (20)

Ip unit 1
Ip unit 1Ip unit 1
Ip unit 1
 
Digital Image Processing
Digital Image ProcessingDigital Image Processing
Digital Image Processing
 
chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdf
chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdfchapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdf
chapter_4_1ST_PART_BASIC_RELATION_OF_PIXELSchapter_2_dip_5th_sem.pdf
 
Chapter 2 Image Processing: Pixel Relation
Chapter 2 Image Processing: Pixel RelationChapter 2 Image Processing: Pixel Relation
Chapter 2 Image Processing: Pixel Relation
 
1513 circles
1513 circles1513 circles
1513 circles
 
On the Odd Gracefulness of Cyclic Snakes With Pendant Edges
On the Odd Gracefulness of Cyclic Snakes With Pendant EdgesOn the Odd Gracefulness of Cyclic Snakes With Pendant Edges
On the Odd Gracefulness of Cyclic Snakes With Pendant Edges
 
E field dipole
E field dipoleE field dipole
E field dipole
 
Vector space
Vector spaceVector space
Vector space
 
On the Zeros of Polar Derivatives
On the Zeros of Polar DerivativesOn the Zeros of Polar Derivatives
On the Zeros of Polar Derivatives
 
Graph
GraphGraph
Graph
 
Geometry (Grid & section formula)
Geometry (Grid & section formula)Geometry (Grid & section formula)
Geometry (Grid & section formula)
 
planes and distances
planes and distancesplanes and distances
planes and distances
 
THREE DIMENSIONAL GEOMETRY
THREE DIMENSIONAL GEOMETRYTHREE DIMENSIONAL GEOMETRY
THREE DIMENSIONAL GEOMETRY
 
Three dim. geometry
Three dim. geometryThree dim. geometry
Three dim. geometry
 
Lines and curves algorithms
Lines and curves algorithmsLines and curves algorithms
Lines and curves algorithms
 
Ch4
Ch4Ch4
Ch4
 
Pixel Relationships Examples
Pixel Relationships ExamplesPixel Relationships Examples
Pixel Relationships Examples
 
Calculus a Functions of Several Variables
Calculus a Functions of Several Variables Calculus a Functions of Several Variables
Calculus a Functions of Several Variables
 
Further pure mathmatics 3 vectors
Further pure mathmatics 3 vectorsFurther pure mathmatics 3 vectors
Further pure mathmatics 3 vectors
 
Vectors mod-1-part-1
Vectors mod-1-part-1Vectors mod-1-part-1
Vectors mod-1-part-1
 

Recently uploaded

1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdfQucHHunhnh
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDThiyagu K
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfsanyamsingh5019
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAssociation for Project Management
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfchloefrazer622
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhikauryashika82
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...EduSkills OECD
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfchloefrazer622
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsTechSoup
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...christianmathematics
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfJayanti Pande
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...fonyou31
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 

Recently uploaded (20)

1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdf
 
APM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across SectorsAPM Welcome, APM North West Network Conference, Synergies Across Sectors
APM Welcome, APM North West Network Conference, Synergies Across Sectors
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdf
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 

pixelrelationships-m-1.pptx.ppt

  • 1.
  • 4. f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - - f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - - f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - - f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - - I I I I I - - - - - I I I I I - - - - -
  • 5.  A Pixel p at coordinates ( x, y) has 4 horizontal and vertical neighbors.  Their coordinates are given by: (x+1, y) (x-1, y) (x, y+1) & (x, y-1) f(2,1) f(0,1) f(1,2) f(1,0)  This set of pixels is called the 4-neighbors of p denoted by N4(p).  Each pixel is unit distance from ( x ,y). f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - - f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - - f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - - f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - - I I I I I - - - - - I I I I I - - - - -
  • 6.  A Pixel p at coordinates ( x, y) has 4 diagonal neighbors.  Their coordinates are given by: (x+1, y+1) (x+1, y-1) (x-1, y+1) & (x-1, y-1) f(2,2) f(2,0) f(0,2) f(0,0)  This set of pixels is called the diagonal-neighbors of p denoted by ND(p).  diagonal neighbors + 4-neighbors = 8-neighbors of p. f(0,0) f(0,1) f(0,2) f(0,3) f(0,4) - - - - - f(1,0) f(1,1) f(1,2) f(1,3) f(1,4) - - - - - f(x,y) = f(2,0) f(2,1) f(2,2) f(2,3) f(2,4) - - - - - f(3,0) f(3,1) f(3,2) f(3,3) f(3,4) - - - - - I I I I I - - - - - I I I I I - - - - -
  • 7. Adjacency: Two pixels are adjacent if they are neighbors and their intensity level ‘V’ satisfy some specific criteria of similarity. e.g. V = {1} V = { 0, 2} Binary image = { 0, 1} Gray scale image = { 0, 1, 2, ------, 255} In binary images, 2 pixels are adjacent if they are neighbors & have some intensity values either 0 or 1. In gray scale, image contains more gray level values in range 0 to 255.
  • 8. 4-adjacency: Two pixels p and q with the values from set ‘V’ are 4-adjacent if q is in the set of N4(p). e.g. V = { 0, 1} 1 1 2 1 1 0 1 0 1 p in RED color q can be any value in GREEN color.
  • 9. 8-adjacency: Two pixels p and q with the values from set ‘V’ are 8-adjacent if q is in the set of N8(p). e.g. V = { 1, 2} 0 1 1 0 2 0 0 0 1 p in RED color q can be any value in GREEN color
  • 10. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) OR (ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels whose values are from ‘V’. e.g. V = { 1 } 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 i
  • 11. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) e.g. V = { 1 } (i) b & c 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I
  • 12. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) e.g. V = { 1 } (i) b & c 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I Soln: b & c are m-adjacent.
  • 13. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) e.g. V = { 1 } (ii) b & e 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I
  • 14. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) e.g. V = { 1 } (ii) b & e 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I Soln: b & e are m-adjacent.
  • 15. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) OR e.g. V = { 1 } (iii) e & i 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 i
  • 16. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in ND(p) & the set N4(p) n N4(q) have no pixels whose values are from ‘V’. e.g. V = { 1 } (iii) e & i 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I
  • 17. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in ND(p) & the set N4(p) n N4(q) have no pixels whose values are from ‘V’. e.g. V = { 1 } (iii) e & i 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I Soln: e & i are m-adjacent.
  • 18. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) OR (ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels whose values are from ‘V’. e.g. V = { 1 } (iv) e & c 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I
  • 19. m-adjacency: Two pixels p and q with the values from set ‘V’ are m-adjacent if (i) q is in N4(p) OR (ii) q is in ND(p) & the set N4(p) n N4(q) have no pixels whose values are from ‘V’. e.g. V = { 1 } (iv) e & c 0 a 1 b 1 c 0 d 1 e 0 f 0 g 0 h 1 I Soln: e & c are NOT m-adjacent.
  • 20. Connectivity: 2 pixels are said to be connected if their exists a path between them. Let ‘S’ represent subset of pixels in an image. Two pixels p & q are said to be connected in ‘S’ if their exists a path between them consisting entirely of pixels in ‘S’. For any pixel p in S, the set of pixels that are connected to it in S is called a connected component of S.
  • 21. Paths: A path from pixel p with coordinate ( x, y) with pixel q with coordinate ( s, t) is a sequence of distinct sequence with coordinates (x0, y0), (x1, y1), ….., (xn, yn) where (x, y) = (x0, y0) & (s, t) = (xn, yn) Closed path: (x0, y0) = (xn, yn)
  • 22. Example # 1: Consider the image segment shown in figure. Compute length of the shortest-4, shortest-8 & shortest-m paths between pixels p & q where, V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 23. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 24. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 25. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 26. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 27. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 28. Example # 1: Shortest-4 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3 So, Path does not exist.
  • 29. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 30. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 31. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 32. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 33. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 34. Example # 1: Shortest-8 path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3 So, shortest-8 path = 4
  • 35. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 36. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 37. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 38. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 39. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 40. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3
  • 41. Example # 1: Shortest-m path: V = {1, 2}. 4 2 3 2 q 3 3 1 3 2 3 2 2 p 2 1 2 3 So, shortest-m path = 5
  • 42. Region: Let R be a subset of pixels in an image. Two regions Ri and Rj are said to be adjacent if their union form a connected set. Regions that are not adjacent are said to be disjoint. We consider 4- and 8- adjacency when referring to regions. Below regions are adjacent only if 8-adjacency is used. 1 1 1 1 0 1 Ri 0 1 0 0 0 1 1 1 1 Rj 1 1 1
  • 43. Boundaries (border or contour): The boundary of a region R is the set of points that are adjacent to points in the compliment of R. 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 RED colored 1 is NOT a member of border if 4-connectivity is used between region and background. It is if 8-connectivity is used.
  • 44.
  • 45.
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51. (1=2)
  • 52.
  • 53. (3=4)
  • 54.
  • 55.
  • 56. (1=5)
  • 57.
  • 58.
  • 59. Distance Measures: Distance between pixels p, q & z with co-ordinates ( x, y), ( s, t) & ( v, w) resp. is given by: a) D( p, q) ≥ 0 [ D( p, q) = 0 if p = q] …………..called reflexivity b) D( p, q) = D( q, p) .………….called symmetry c) D( p, z) ≤ D( p, q) + D( q, z) ..………….called transmitivity Euclidean distance between p & q is defined as- De( p, q) = [( x- s)2 + (y - t)2]1/2
  • 60. City Block Distance: The D4 distance between p & q is defined as D4( p, q) = |x - s| + |y - t| In this case, pixels having D4 distance from ( x, y) less than or equal to some value r form a diamond centered at ( x, y). 2 2 1 2 2 1 0 1 2 2 1 2 2 Pixels with D4 distance ≤ 2 forms the following contour of constant distance.
  • 61. Chess-Board Distance: The D8 distance between p & q is defined as D8( p, q) = max( |x - s| , |y - t| ) In this case, pixels having D8 distance from ( x, y) less than or equal to some value r form a square centered at ( x, y). 2 2 2 2 2 2 1 1 1 2 2 1 0 1 2 2 1 1 1 2 2 2 2 2 2 Pixels with D8 distance ≤ 2 forms the following contour of constant distance.
  • 62.
  • 63.
  • 64.  The AND operator is usually used to mask out part of an image.
  • 65.  Parts of another image can be added with a logical OR operator.
  • 66. Result of AND Result of OR OR