CS 900 - Seminar Presentation
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Transcript of CS 900 - Seminar Presentation
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CS 900 - Graduate Seminar(Spring / Summer 2015)
Pattern Recognition
- Baabu Aravind Vellaian Selvarajan
200339484
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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 Buildinga machine thatcan recognizingpatterns
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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 aphysicalobject
E.g. Visual, Temporal, Musical, Logical.. Etc.,
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Pattern Class
A set of patterns sharing common attributes [5]
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What is meant by Recognition ?
Discover to which class of entities the patternbelongs and the name of thepattern
Also its different fromidentification[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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https://www.projectrhea.org/rhea/index.php/Introduction_To_Pattern_Recognition_and_Classification -
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
Classificationusing 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 PDAsPrinted 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 cameraSegment isolated fishExtract features from each fish
- Length, width, brightness, etc.,Classify Each fish
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Case Study
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Conclusion
What happens if a customer finds SeaBassin thereSalmoncan ? (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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https://en.wikipedia.org/wiki/Pattern_recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttp://www.slideshare.net/lgustavomartins/introduction-to-pattern-recognitionhttps://en.wikipedia.org/wiki/Machine_perceptionhttps://www.coursera.org/course/mlhttp://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/MaazHasan/pattern-recognition-37839488?qid=bf185e66-d1da-421f-8325-931165941321&v=qf1&b=&from_search=30http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1http://www.slideshare.net/Randa_Elanwar/what-is-patternrecognition-lecture-1https://www.projectrhea.org/rhea/index.php/Introduction_To_Pattern_Recognition_and_Classificationhttps://www.projectrhea.org/rhea/index.php/Introduction_To_Pattern_Recognition_and_Classificationhttp://homepage.tudelft.nl/a9p19/papers/4PR_Approaches.pdf -
Thanks for your patience