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PATTERN
RECOGNITION
Team teaching1
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OUTLINES
Whats is pattern? What is class pattern? What is pattern recognition? Human perception Application example Statistically way Human and machine perception Pattern recognition process Topic Searching
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WHAT IS A PATTERN?
A pattern is an abstract object, or a set ofmeasurements describing a physical object.
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WHAT IS A PATTERN CLASS?
A pattern class (or category) is a set ofpatterns sharing common attributes.
A collection of similar (not necessarilyidentical) objects.
During recognition given objects are assignedto prescribed classes.
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WHAT IS PATTERN RECOGNITION?
Theory, Algorithms, Systems to put Patternsinto Categories
Relate Perceived Pattern to PreviouslyPerceived Patterns
Learn to distinguish patterns of interest fromtheir background
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HUMAN PERCEPTION
Humans have developed highly sophisticatedskills 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 tomachines.
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EXAMPLES OF APPLICATIONS
Handwritten: sorting letters by postal code.Printed texts: reading machines for blindpeople, digitalization of text documents.
Optical CharacterRecognition
(OCR)
Face recognition, verification, retrieval.Finger prints recognition.Speech recognition.Biometrics
Medical diagnosis: X-Ray, EKG(ElectroCardioGraph) analysis.
Diagnostic
systems
Automated Target Recognition (ATR).Image segmentation and analysis (recognitionfrom aerial or satelite photographs).
Militaryapplications
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PATTERN RECOGNITION APPLICATIONS
BY PROBLEM DOMAINS
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PATTERN RECOGNITION MODEL
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THESTATISTICAL
WAY10
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GRID BY GRID COMPARISON
AA B
Grid by Grid
Comparison
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GRID BY GRID COMPARISON
AA B
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0 0 1 00 0 1 00 1 1 11 0 0 11 0 0 1
0 1 1 00 1 1 00 1 1 01 0 0 11 0 0 1
No ofMismatch= 3
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GRID BY GRID COMPARISON
AA B
Grid by Grid
Comparison
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GRID BY GRID COMPARISON
AA B
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0 0 1 00 0 1 00 1 1 11 0 0 11 0 0 1
1 1 1 00 1 0 10 1 1 10 1 0 11 1 1 0
No ofMismatch= 9
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PROBLEM WITH GRID BY GRIDCOMPARISON
Time to recognize a pattern- Proportional tothe number of stored patterns ( Too costly
with the increase of number of patterns
stored )
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SolutionArtificial
Intelligence
A-Z a-z 0-9
*/-+1@#
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HUMAN AND MACHINE PERCEPTION
We are often influenced by the knowledge of howpatterns are modeled and recognized in nature when we
develop pattern recognition algorithms.
Research on machine perception also helps us gaindeeper understanding and appreciation for pattern
recognition systems in nature.
Yet, we also apply many techniques that are purelynumerical and do not have any correspondence innatural systems.
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PATTERN RECOGNITION
Two Phase : Learningand Detection.
Time to learn is higher. Driving a car
Difficult to learn but once learnt it becomesnatural.
Can use AI learning methodologies such as: Neural Network. Machine Learning.
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BASIC CONCEPT
- Cannot be directlymeasured.
- Patterns with equalhidden state belong to
the same class.
Feature vector-Patterns with equal
hidden state belong tothe same class.
Task- To design aclassifer (decision
rule) which decidesabout a hidden state
based on an
observation.
Hidden state Feature vector
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EXAM
PLE
Task: jockey-hoopsterrecognition.
The set of hiddenstate Y is
{H,J}
The feature space is
X2
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LEARNING
How can machine learn the rule from data?
Supervised learning: a teacher provides a category label orcost for each pattern in the training set.
Unsupervised learning: the system forms clusters or naturalgroupings of the input patterns.
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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)
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PATTERN RECOGNITION PROCESS(CONT.)
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Post- processing
Classification
FeatureExtraction
Segmentation
Sensing
input
Decision
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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. 23
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Sistem PR
Sensors andpreprocessing
Feature
extractionClassifier
Class
assignment
Learning algorithmTeacher
Pattern
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CASE STUDY
Fish Classification: Sea Bass / Salmon.
Problem: Sorting incoming fishon 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
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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 fish3.Take measurements4.Make decision
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CASE STUDY (CONT.)
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Classification
FeatureExtraction
Pre-processing
Sea Bass Salmon
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CASE STUDY (CONT.)
Pre-Processing: Image enhancement Separating touching or occluding fish. Finding the boundary of the fish.
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HOW TO SEPARATESEA 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 isgenerally longer than a salmon.
Even though sea bass is longer than salmon on theaverage, there are many examples of fish where this
observation does not hold.29
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HOW TO SEPARATE
SEA BASS FROM SALMON?
To improve recognition, we might have to usemore than one feature at a time. Single features might not yield the best performance. Combinations of features might yield better performance.
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1
2
x
x
1
2
:
:
x lightness
x width
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FEATURE SELECTION
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Good
features Bad features
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DECISION BOUNDARY
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DECISION BOUNDARY (CONT.)
33More complex model result more complex boundary
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DECISION BOUNDARY (CONT.)
34Different criteria lead to different decision boundaries
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DECISION BOUNDARY (CONT.)
What if a customers find Sea bass in thereSalmon can?
We should also consider costs of differenterrors we make in our decisions.
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DECISION BOUNDARY (CONT.)
For example, if the fish packing companyknows that:
Customers who buy salmon will object vigorouslyif they see sea bass in their cans.
Customers who buy sea bass will not be unhappyif they occasionally see some expensive salmon in
their cans.
How does this knowledge affect our decision?
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CASE STUDY (CONT.)
Issues with feature extraction: Correlated features do not necessary improve
performance.
It might be difficult to extract certain features. It might be computationally expensive to extract
many features.
Missing Features. Domain Knowledge.
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THE DESIGN CYCLE
Collecting training and testing data.Collect Data
Domain dependence.
Chose Features.
Domain dependence.Chose Model
Supervised learningUnsupervised learning.
Train
Performance with future dataEvaluate
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Q & A39
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TOPIC
SEARCHING
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