neural - fuzzy logic for automatic object recognition

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NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION .

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NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION. INTRODUCTION. Artificial Neural Networks is a system modeled on the human brain. It is an attempt to simulate with specialized hardware or sophisticated software the multiple layers of simple processing elements called neurons. - PowerPoint PPT Presentation

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Page 1: NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION

NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT

RECOGNITION.

Page 2: NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION

INTRODUCTION

• Artificial Neural Networks is a system modeled on the human brain. It is an attempt to simulate with specialized hardware or sophisticated software the multiple layers of simple processing elements called neurons.

• Fuzzy set theory resembles human reasoning in its use of approximate or corrupted data to generate decision.

• Integration of ANN and Fuzzy logic can provide a very efficient solution for Target recognition.

Page 3: NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION

MODULES OF THE ATR SYSTEM

• Acquisition: Capturing data with a sensor.• Transformation: Preprocessing of the image.• Segmentation: Identifying regions of interest, using

Freeman code for border detection.• Geometric features: Usage of Hough Transform for

identifying hidden lines also.• Target Database: Tabulated values for each model.• Model Matching: Matching measured values with

tabulated values.

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IMPLEMENTATION OF NEURAL - FUZZY LOGIC

• With the inclusion of fuzzy logic in the ATR system, even when one dimension is obscured a match can be made with the remaining two dimensions.

• Network is trained using a Back Propagation algorithm

Page 5: NEURAL - FUZZY LOGIC FOR AUTOMATIC OBJECT RECOGNITION

BACK PROPAGATION ALGORITHM

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OTHER APPLICATIONS

• Character Recognition

• Automatic Phonetic Recognition

• Facial Recognition

• Signature Recognition

• Fingerprint Recognition

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CONCLUSION• The computing world has a lot to gain from

Neural Networks.• Their ability to learn makes them flexible and very

powerful.• The most exciting aspect of Neural Networks is

the possibility that some day ‘conscious’ networks may be produced.

• Neural Networks have a huge potential and we will get the best of them when integrated with computing, AI, Fuzzy logic and related subjects.

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BIBLIOGRAPHY

BOOKS:

Digital Image Processing – Rafael C. Gonzalez / Richard E. Woods

Neural Networks – James A. Freeman / David M. Skapura

WEB SITES:

http://www.dacs.dtic.mil/techs/neural/neural.title.html

http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html

http://www.elsevier.nl/locate/patrec

http://www.mathworks.com/products/image/description

http://www.cse.msu.edu

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PRESENTED BY

NAGINI INDUGULA (4/4 CSE) 98311A0515

Sree Nidhi Institute of Science and Technology Hyderabad

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