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 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