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Demetz Clément. Lip-recognition Software using a Kohonen Algorithm for Image Compression. ECE 539 Final Project Fall 2003. Outline. -Problem and motivation -Data creation: preprocessing -Kohonen self organization map (SOM) -Multi-Layer perceptron -Final results -Conclusion -References.

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Lip-recognition Software using a Kohonen Algorithm for Image Compression

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Lip recognition software using a kohonen algorithm for image compression l.jpg

Demetz Clément

Lip-recognition Software using a Kohonen Algorithm for Image Compression

ECE 539

Final Project Fall 2003


Outline l.jpg

Outline

-Problem and motivation

-Data creation: preprocessing

-Kohonen self organization map (SOM)

-Multi-Layer perceptron

-Final results

-Conclusion

-References


Problem l.jpg

  • Problem of voice recognition:

  • A combined approach always leads to better results

Problem

For cell phone and PDA: voice recognition and visual recognition

Lip-recognition

Combined recognition

Voice-recognition


Problem of lip recognition software l.jpg

Problem of lip-recognition software

  • Need high computational power.

  • Need to be implement on low-power systems (PDA, cell phone)

  • How can we reduce the size of the information?

  • Pb: Find a way to implement such an algorithm with few computation.


Motivation l.jpg

Reduce the size of the image with a Kohonen Self organization map

Motivation

Filter

Kohonen SOM

Image of a cell phone digital camera

Contour of the mouth

Multi-Layer perceptron


Preprocessing l.jpg

-Starting with low quality JPEG pictures

-Gradient filters are applied to only keep the contour of the mouths.

-the opening of the mouth is a relevant input: needs to follow a certain pattern to pronounce a sound.

Preprocessing

Dark part of the mouth

Contour of the dark part

JPEG picture of the mouth

Pb: a contour corresponds to thousands points: it is still too large to have a low computation time


Kohonen self organisation map som l.jpg

Kohonen Self Organisation Map (SOM)

  • -Idea of using a Kohonen self organization map to reduce the information to 12 neurons

  • problems:

  • Initialization

  • Bad stretching or turning of the SOM


Kohonen som l.jpg

  • problems:

  • Initialization

  • Bad stretching or turning of the SOM

Kohonen SOM

We want to keep all the information: here we are losing the left part


Kohonen som9 l.jpg

Kohonen SOM

  • A way to avoid problems:

  • We link the first and the last neurons


Kohonen som10 l.jpg

Kohonen SOM

  • Results of the Kohonen Map: we keep 12 points representing the contour:


Multi layer perceptron l.jpg

Multi-Layer perceptron

  • We take the 12 points given by the SOM as inputs. SOM applied many times on each picture to create the database

  • 3 classes of pictures: only 3 sounds, because the lip-recognition is a support to a voice recognition

  • Training on 15 pictures, testing on 3 pictures.


Multi layer perceptron result l.jpg

Multi-Layer perceptron: Result

100% Classification rate is obtained


Multi layer perceptron result13 l.jpg

Multi-Layer perceptron: Result

100% Classification rate is obtained

With a 400 iterations training.


Conclusion l.jpg

Conclusion

  • Kohonen SOM reduces the problem to a 12 dimension problem (previously, working on pictures mean thousands dimension) .

  • Multi-Layer perceptron needs a training, but once it is trained computations are made very fast.

  • we can obtain a 100% classification rate with 3 sounds.

  • Pb: because of Matlab, transforming picture into Matrix needs computations. (solution: use another language more picture processing-oriented)


Some references l.jpg

Some references

-Image compression by Self-Organized kohonen Map

Christophe Amerijckx, Philippe Thissen..IEE Transition on Neural Networks 1998.

http://www.dice.ucl.ac.be/~verleyse/papers/ieeetnn98ca.pdf

-SRAM bitmap shape recognition and sorting Using Neural Networks.

Randall S. Collica. IEEE.

http://www.ibexprocess.com/solutions/wp_SRAM.pdf

-From your lips to your printer.

James Fallow.

-SRAM bitmap shape recognition and sorting using neural networks.Collica, R.S., Card, J.P., and Martin.

W. ISBN 0894-6507

-A kohonen Neural Network Controlled All-optical router system.

E.E.E Frietman, M.T. Hill, G.D. Khoe.

http://www.ph.tn.tudelft.nl/~ed/pdfs/IJCR.pdf


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