Cs 900 seminar presentation
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Transcript of Cs 900 seminar presentation
CS 900 - Graduate Seminar(Spring / Summer – 2015)
Pattern Recognition
- Baabu Aravind Vellaian Selvarajan
200339484
Introduction
• Pattern Recognition is a branch of ArtificialIntelligence (Machine Learning) [1]
• PR is an area of AI deals with recognition ofpatterns and regularities in data to solveproblems using computable machines
AIPR
AI
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Human Perception
• Humans have developed highly sophisticatedskills for sensing their environment and takingaccording to their observation [2]
• E.g. Recognizing a face, Understanding Spokenlanguage, Reading Handwriting, Smell of food
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Machine Perception
• The capability of machines to interpret data ina manner that is similar to the way humanuses their senses to relate the world around [3]
• Simply we can say “Building a machine thatcan recognizing patterns”
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Machine Leaning
• What is machine learning ?
Machine learning is the science of gettingcomputers to act without being explicitlyprogrammed [4].
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What is Pattern ?
• A set of features of individual objects
• It is an abstraction, represented by a set ofmeasurements describing a “physical” object
• E.g. Visual, Temporal, Musical, Logical.. Etc.,
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What is meant by Recognition ?
• Discover to which class of entities the “pattern”belongs and the name of the “pattern”
• Also its different from “identification” [6]
• For Example: Security system searching database fora person
• finding similar one isface identification• searching several picsof a particular personand allowing him is facerecognition
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Pattern Recognition
It is the study of how machine can
Perceive + Process + Prediction [2]
• Perceive : Interaction with the real-world (i.e.,observing the environment)
• Process : Learn to distinguish patterns ofinterest from their background
• Predication : Making reasonable decisionsabout the categories of patterns
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Pattern Recognition
Two phase process
Leaning / Training and Detecting / Classifying
Learning:– its time consuming and hard process
– Several examples of each class must be exposed to thesystem
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Classification Algorithm
It is otherwise called as supervised learning
A teacher provides a category label to train a classifier [2]
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Clustering Algorithm
It is otherwise called as unsupervised learning
System forms clusters or natural groupings of input patterns based on some similar criteria [2]
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Pattern Recognition System
https://www.projectrhea.org/rhea/index.php/Introduction_To_Pattern_Recognition_and_Classification
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Pattern Recognition System
• Sensing – which collects data, the measurementof physical variables
• Segmentation – Isolation of pattern of interestfrom background and removal of noise from thedata
• Feature Extraction – in terms of features findinga new representation
• Classification – using features assign the input tothe category or class
• Post-processing – making decision using thefeatures and classification
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Applications
Optical Character Recognition
Hand Written: sorting letters, input device for PDA’sPrinted Texts : digitalization of text documents and reading machines for blind people
Biometrics Face Recognition, Verification, RetrievalFinger Print RecognitionSpeech Recognition
Diagnostic systems Medical Diagnosis: X-Ray, Electro Cardio Graph analysis
Military applications
Automated Target RecognitionImage segmentation and analysis – recognition from aerial or satellite photographs
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Approach
• Statistical Model : Pattern recognition systemsare based on statistics and probabilities
• Syntactic Model / Structural Model: Based onrelation between features, patterns arerepresented by structures
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Approach
• Template matching model: a template or aprototype of the pattern to be recognized isavailable
• Neural Network Model: able to learn andresolve complex problems based on availableknowledge.
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Case Study
• Source
–Pattern Classification – 2nd Edition Bookby Richard Duda and Peter Hart
• Problem
–A fish packing plant wants to automate theprocess of sorting incoming fish on aconveyor according to species using opticalsensing
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Case Study
• Fish Classification
–Considering only two types of fishes
– SeaBass / Salmon
• Camera has been set up for sensing – taking pictures of the incoming fish
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Case Study
• What can cause problems during sensing ?– Lighting conditions
– Position of fish on conveyor belt
– Camera noise, etc.,
• What are the steps in process ?– Capture image
– Isolate fish
– Take measurements
– Make Decisions
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Case Study
• What kind of information can distinguish one species for the other ?– Length
– Lightness
– Width
– Number and shape of fins
– Position of the mouth, Etc.,
• Additional info from a fisherman “SeaBass” is generally longer than a “Salmon”
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Case Study
•Preprocess raw data from camera•Segment isolated fish•Extract features from each fish
- Length, width, brightness, etc.,•Classify Each fish
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Conclusion
• What happens if a customer finds “Sea Bass”in there “Salmon” can ? (unhappy, costly price)
• We Should also consider cost of differenterrors we make in our decisions
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References
[1]. https://en.wikipedia.org/wiki/Pattern_recognition
[2]. http://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognition
[3]. https://en.wikipedia.org/wiki/Machine_perception
[4]. https://www.coursera.org/course/ml
[5]. http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30
[6]. http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1
[7].https://www.projectrhea.org/rhea/index.php/Introduction_To_Pattern_Recognition_and_Classification
[8]. http://homepage.tudelft.nl/a9p19/papers/4PR_Approaches.pdf
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