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.
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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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
13
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)
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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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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