It is an introductory slide for pattern recolonization. This presentation was quit emotional for me because it was the last academic presentation in Green University of Bangladesh. For that i have used a sad emo at the first of the slide.

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Green University of Bangladesh
Presented by:
Ajharul Abedeen
Department of Computer Science and Engineering

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PATTERN
RECOGNITION
Introductory Facts
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4. Contents
 What is a pattern?
 What is A pattern Class?
 What is pattern recognition?
 Human Perception
 Examples of applications
 Human and Machine Perception
 Pattern Recognition
 Pattern Recognition Process
 Case Study
 Tools
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5. WHAT IS A PATTERN?
 A pattern is an abstract object, or a set of
measurements describing a physical object.
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6. WHAT IS A PATTERN CLASS?
 A pattern class (or category) is a set of patterns
sharing common attributes.
 A collection of “similar” (not necessarily identical)
objects.
 During recognition given objects are assigned to
prescribed classes.
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7. WHAT IS PATTERN RECOGNITION?
 Theory, Algorithms, Systems to put Patterns into
Categories
 Relate Perceived Pattern to Previously Perceived
Patterns
 Learn to distinguish patterns of interest from their
background
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8. HUMAN PERCEPTION
 Humans have developed highly sophisticated skills
for sensing their environment and taking actions
according to what they observe, e.g.,
 Recognizing a face.
 Understanding spoken words.
 Reading handwriting.
 Distinguishing fresh food from its smell.
 We would like to give similar capabilities to
machines.
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9. EXAMPLES OF APPLICATIONS
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10. GRID BY GRID COMPARISON
A A B
Grid by Grid
Comparison
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11. GRID BY GRID COMPARISON
A A B
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0 0 1 0
0 0 1 0
0 1 1 1
1 0 0 1
1 0 0 1
0 1 1 0
0 1 1 0
0 1 1 0
1 0 0 1
1 0 0 1
No of
Mismatch= 3

12. GRID BY GRID COMPARISON
A A B
Grid by Grid
Comparison
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13. GRID BY GRID COMPARISON
A A B
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0 0 1 0
0 0 1 0
0 1 1 1
1 0 0 1
1 0 0 1
1 1 1 0
0 1 0 1
0 1 1 1
0 1 0 1
1 1 1 0
No of
Mismatch= 9

14. HUMAN AND MACHINE PERCEPTION
 We are often influenced by the knowledge of how patterns are
modeled and recognized in nature when we develop pattern
recognition algorithms.
 Research on machine perception also helps us gain deeper
understanding and appreciation for pattern recognition
systems in nature.
 Yet, we also apply many techniques that are purely numerical
and do not have any correspondence in natural systems.
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15. PATTERN RECOGNITION
 Two Phase : Learning and Detection.
 Time to learn is higher.
 Driving a car
 Difficult to learn but once learnt it becomes natural.
 Can use AI learning methodologies such as:
 Neural Network.
 Machine Learning.
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16. LEARNING
 How can machine learn the rule from data?
 Supervised learning: a teacher provides a category label or cost for
each pattern in the training set.
 Unsupervised learning: the system forms clusters or natural
groupings of the input patterns.
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17.  Classification (known categories)
 Clustering (creation of new categories)
CLASSIFICATION VS. CLUSTERING
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Category “A”
Category “B”
Clustering
(Unsupervised Classification)
Classification
(Supervised Classification)

18. PATTERN RECOGNITION PROCESS (CONT.)
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Post- processing
Classification
Feature Extraction
Segmentation
Sensing
input
Decision

19. PATTERN RECOGNITION PROCESS
 Data acquisition and sensing:
 Measurements of physical variables.
 Important issues: bandwidth, resolution , etc.
 Pre-processing:
 Removal of noise in data.
 Isolation of patterns of interest from the background.
 Feature extraction:
 Finding a new representation in terms of features.
 Classification
 Using features and learned models to assign a pattern to a
category.
 Post-processing
 Evaluation of confidence in decisions.
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20. CASE STUDY
 Fish Classification:
 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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Salmon
Sea-bass

21. 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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22. CASE STUDY (CONT.)
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Classification
Feature Extraction
Pre-processing
“Sea Bass” “Salmon”

23. CASE STUDY (CONT.)
 Pre-Processing:
 Image enhancement
 Separating touching or occluding fish.
 Finding the boundary of the fish.
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24. 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 …
 Assume a fisherman told us that a “sea bass” is generally
longer than a “salmon”.
 Even though “sea bass” is longer than “salmon” on the
average, there are many examples of fish where this
observation does not hold.
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Image Tagging.
Friend Suggestion
Product Suggestion
Social Networking Engines

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Tools
MATLAB
R
WEKA

27. Q & A
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28. THANK
YOU
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