Introduction to Pattern recognition

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PATTERN
RECOGNITION
Muhammad Haroon
Lecturer
University of Gujrat Lahore Sub Campus
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Muhammad Haroon (Lecturer, UOG LHR)

WHAT IS A PATTERN?
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A pattern is a regularity in the world,
man-made design, or abstract ideas.

WHAT IS PATTERN RECOGNITION?
Pattern Recognition is the process of
distinguishing and segmenting data according to
set criteria or by common elements, which is
performed by special algorithms.
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TYPE OF PATTERNS
1. Crystal Pattern (Atomic/Molecular)
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2. Pattern of Constellation (2D)
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3. Finger Print
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4. Facial Pattern
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Factors Plays role in Face
Recognition
i. Distance between both eyes
ii. Distance between forehead to chin
iii. Moustache / Beard
iv. Eye Retina Color and Size
v. Facial Expressions
vi. Width of Lips
… many other
factors
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Face Detection in Picture
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5. Biological Patterns
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6. Animal Skin Patterns
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EXAMPLES OF APPLICATIONS
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How does Pattern Recognition work?
1. Data is gathered from its sources.
2. Data is cleaned up from noise.
3. Information is examined for relevant features or
common elements
4. These elements are subsequently grouped in specific
segments;
5. The segments are analyzed for insights into data sets;
6. The extracted insights are implemented into the
business operation.
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THE
STATISTICAL
WAY
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GRID BY GRID COMPARISON
B
A
AGrid by Grid
Comparison
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B
A
A No of
Mismatch= 3
0 1 1 0
0 0 1
0
0
1
1
1
1
0
0
0
0
0
1
1
1
1 1 1 1
1 1 0 16
1 0 0 1
1 0 0
0
0
1
1
1

B
A
A Grid by Grid
Comparison
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B
A
A No of
Mismatch= 9
0 0 1 1 1 1
0
0
0
1
1
1
1 0 1
1 1 1
1 0 0 1 0 1
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1 0 0 1 1 1
0
0
0
1
0
0
0
1
1
1

CLASSIFICATION VS. CLUSTERING
Classification (known categories)
Clustering (creation of new categories)
Classification
(Supervised Classification)
Clustering
(Unsupervised Classification) 19
Category “A”
Category “B”

CASE STUDY
Fish Classification:
Salmon
Sea Bass / Salmon.
Problem: Sorting incoming fish
on a conveyor belt according to
species.
Assume that we have only two kinds of fish:
Sea bass.
Salmon.
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Sea-bass

CASE STUDY (CONT.)
What can cause problems during sensing?
Lighting conditions.
Position of fish on the conveyor belt.
Camera noise.
etc…
What are the steps in the process?
1. Capture image.
2. Isolate fish
3. Take measurements
4. Make decision
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CASE STUDY (CONT.)
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Pre-processing
Feature Extraction
Classification
“Sea Bass” “S almon”

CASE STUDY (CONT.)
Pre-Processing:
Image enhancement
Separating touching or occluding fish.
Finding the boundary of the fish.
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HOW TO SEPARATE
SEA BASS FROM SALMON?
Possible features to be used:
Length
Lightness
Width
Number and shape of fins
Position of the mouth
Etc …
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HOW TO SEPARATE
SEA BASS FROM SALMON?
To improve recognition, we might have to use
more than one feature at a time.
Single features might not yield the best performance.
Combinations of features might yield better performance.
x1 : lightness
x2 : width
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DECISION BOUNDARY
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DECISION BOUNDARY (CONT.)
More complex model result more complex boundary 27

DECISION BOUNDARY (CONT.)
Different criteria lead to different decision boundaries 28

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FACE DETECTION IN PICTURE EXAMPLE
SAAD WITH HIS FRIENDS QUAID AND LIAQAT BRO

Introduction to Pattern recognition

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