The document provides an introduction to pattern recognition. It defines what a pattern and pattern class are, and explains that pattern recognition involves categorizing patterns based on common attributes. It then discusses examples of pattern recognition applications in areas like OCR, biometrics, medical diagnosis, and military uses. The document outlines the process of pattern recognition including sensing data, segmentation, feature extraction, classification, and post-processing. It also discusses supervised vs. unsupervised learning and the design cycle of collecting data, choosing features and models, training, and evaluating.

1. WELCOME EVERYONE
Good Afternoon

2. Pattern Recognition
Swarnava Sen
MCKV Institute of Engineering, Liluah
IT/2014/028

3. What is pattern ?
o A pattern is an abstract object, or a set of measurements
describing a physical object.

4. What is a pattern CLass?
o A pattern class (or category) is a set of patterns sharing common
attributes.
o A collection of “Similar” (not necessarily identical) objects.
o During recognition given objects are assigned to prescribed
classes.

5. What is pattern
reCOGnitiOn ?
o Theory, Algorithms, Systems to put Patterns
into Category.
o Relate Perceived Pattern to Previously Perceived
Patterns.
o Learn to distinguish patterns of interest from
their background.

6. hUMan perCeptiOn
o Humans have developed highly sophisticated skills for
sensing their environment and taking actions according to
what they observe, e.g.,
o Recognizing a face.
o Understanding spoken words.
o Reading handwriting.
o Distinguishing fresh food from its smell.
o We would like to give similar capabilities to machines.

7. • Handwritten: sorting letters by postal code.
• Printed texts: reading machines for blind people,
digitalization of text documents.
Optical Character
Recognition
(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 (recognition
from aerial or satelite photographs).
Military
applications
ExamplEs of applications

8. Grid by Grid comparison

9. Grid by Grid comparison
A A B
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

10. Grid by Grid comparison
A A BGrid by Grid
Comparison

11. Grid by Grid comparison
A A B
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

12. problEm with Grid by Grid comparison
o Time to recognize a pattern - Proportional to
the number of stored patterns (Too costly
with the increase of number of patterns
stored)
A-Z a-z 0-9
Solution
Artificial
Intelligence
*/-+1@#

13. pattern recognition
o Two Phase : Learning and Detection.
o Time to learn is higher.
• Driving a car
o Difficult to learn but once learnt it becomes
natural.
o Can use AI learning methodologies such as:
• Neural Network.
• Machine Learning.

14. 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.

15. o Classification (known categories)
o Clustering (creation of new categories)
cLassification vs. cLustering
Category “A”
Category “B”
Clustering
(Unsupervised Classification)
Classification
(Supervised Classification)

16. pattern recognition process
Post- processing
Classification
Feature Extraction
Segmentation
Sensing
input
Decision

17. pattern recognition process
o Sensing:
• Measurements of physical variables.
• Important issues: bandwidth, resolution , etc.
o Segmentation:
• Removal of noise in data.
• Isolation of patterns of interest from the background.
o Feature extraction:
• Finding a new representation in terms of features.
o Classification
• Using features and learned models to assign a pattern to a
category.
o Post-processing
• Evaluation of confidence in decisions.
Post- processing
Classification
Feature Extraction
Segmentation
Sensing
input
Decision

18. • Collecting training and testing data.
Collect Data
• Domain dependence.
Chose Features.
• Domain dependence.
Chose Model
• Supervised learning
• Unsupervised learning.
Train
• Performance with future data
Evaluate
the Design cycLe

19. Demo
o Online face detector demo:

20. Demo (Cont.)
 With my friend “Albert Einstein”

21. tHAnK
YoU