27-08-2012, 01:30 PM
Pattern Recognition
Pattern Recognition.ppt (Size: 564 KB / Downloads: 136)
What is pattern recognition?
Definition from Duda, et al. – the act of taking in raw data and taking an action based on the “category” of the pattern
We gain an understanding and appreciation for pattern recognition in the real world – visual scenes, noises, etc.
Human senses: sight, hearing, taste, smell, touch
Recognition not an exact match like a password
Problem Analysis
Set up a camera and take some sample images to extract features
Length
Lightness
Width
Number and shape of fins
Position of the mouth, etc…
Pattern Classification System
Preprocessing
Segment (isolate) fishes from one another and from the background
Feature Extraction
Reduce the data by measuring certain features
Classification
Divide the feature space into decision regions
Features
We might add other features that are not highly correlated with the ones we already have. Be sure not to reduce the performance by adding “noisy features”
Ideally, you might think the best decision boundary is the one that provides optimal performance on the training data (see the following figure)
Baysian Decision Theory
Pure statistical approach – parametric
Assumes the underlying probability structures are known perfectly
Makes theoretically optimal decisions
Pattern Recognition Stages
Sensing
Use of a transducer (camera or microphone)
PR system depends on the bandwidth, the resolution sensitivity distortion of the transducer
Preprocessing
Segmentation and grouping - patterns should be well separated and not overlap
Feature extraction
Discriminative features
Invariant features with respect to translation, rotation, and scale
Classification
Use the feature vector provided by a feature extractor to assign the object to a category
Post Processing
Exploit context-dependent information to improve performance
Data Collection
How do we know when we have collected an adequately large and representative set of examples for training and testing the system?
Choice of Features
Depends on the characteristics of the problem domain
Simple to extract, invariant to irrelevant transformations, insensitive to noise
Learning and Adaptation
Supervised learning
A teacher provides a category label for each pattern in the training set
Unsupervised learning
The system forms clusters or “natural groupings” of the unlabeled input patterns
Introductory example conclusion
Reader may be overwhelmed by the number, complexity, and magnitude of the sub-problems of Pattern Recognition
Many of these sub-problems can indeed be solved
Many fascinating unsolved problems still remain
DPS Dissertations
PR systems are used in many areas of research
DPS dissertations that used PR systems
Visual systems – Rick Bassett, Sheb Bishop, Tom Lombardi
Speech recognition – Jonathan Law
Handwriting – Mary Manfredi
NLP – Bashir Ahmed
Keystroke Biometric – Mary Curtin, Mary Villani, Mark Ritzmann, Robert Zack
Fundamental research areas – Kwang Lee, Carl Abrams
DPS dissertations in progress using PR systems
Ted Markowitz, John Galatti
Pattern Recognition.ppt (Size: 564 KB / Downloads: 136)
What is pattern recognition?
Definition from Duda, et al. – the act of taking in raw data and taking an action based on the “category” of the pattern
We gain an understanding and appreciation for pattern recognition in the real world – visual scenes, noises, etc.
Human senses: sight, hearing, taste, smell, touch
Recognition not an exact match like a password
Problem Analysis
Set up a camera and take some sample images to extract features
Length
Lightness
Width
Number and shape of fins
Position of the mouth, etc…
Pattern Classification System
Preprocessing
Segment (isolate) fishes from one another and from the background
Feature Extraction
Reduce the data by measuring certain features
Classification
Divide the feature space into decision regions
Features
We might add other features that are not highly correlated with the ones we already have. Be sure not to reduce the performance by adding “noisy features”
Ideally, you might think the best decision boundary is the one that provides optimal performance on the training data (see the following figure)
Baysian Decision Theory
Pure statistical approach – parametric
Assumes the underlying probability structures are known perfectly
Makes theoretically optimal decisions
Pattern Recognition Stages
Sensing
Use of a transducer (camera or microphone)
PR system depends on the bandwidth, the resolution sensitivity distortion of the transducer
Preprocessing
Segmentation and grouping - patterns should be well separated and not overlap
Feature extraction
Discriminative features
Invariant features with respect to translation, rotation, and scale
Classification
Use the feature vector provided by a feature extractor to assign the object to a category
Post Processing
Exploit context-dependent information to improve performance
Data Collection
How do we know when we have collected an adequately large and representative set of examples for training and testing the system?
Choice of Features
Depends on the characteristics of the problem domain
Simple to extract, invariant to irrelevant transformations, insensitive to noise
Learning and Adaptation
Supervised learning
A teacher provides a category label for each pattern in the training set
Unsupervised learning
The system forms clusters or “natural groupings” of the unlabeled input patterns
Introductory example conclusion
Reader may be overwhelmed by the number, complexity, and magnitude of the sub-problems of Pattern Recognition
Many of these sub-problems can indeed be solved
Many fascinating unsolved problems still remain
DPS Dissertations
PR systems are used in many areas of research
DPS dissertations that used PR systems
Visual systems – Rick Bassett, Sheb Bishop, Tom Lombardi
Speech recognition – Jonathan Law
Handwriting – Mary Manfredi
NLP – Bashir Ahmed
Keystroke Biometric – Mary Curtin, Mary Villani, Mark Ritzmann, Robert Zack
Fundamental research areas – Kwang Lee, Carl Abrams
DPS dissertations in progress using PR systems
Ted Markowitz, John Galatti