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Lip-recognition Software using a Kohonen Algorithm for Image Compression

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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  1. Demetz Clément Lip-recognition Software using a Kohonen Algorithm for Image Compression ECE 539 Final Project Fall 2003

  2. Outline -Problem and motivation -Data creation: preprocessing -Kohonen self organization map (SOM) -Multi-Layer perceptron -Final results -Conclusion -References

  3. 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

  4. 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.

  5. 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

  6. -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

  7. 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

  8. 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

  9. Kohonen SOM • A way to avoid problems: • We link the first and the last neurons

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

  11. 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.

  12. Multi-Layer perceptron: Result 100% Classification rate is obtained

  13. Multi-Layer perceptron: Result 100% Classification rate is obtained With a 400 iterations training.

  14. 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)

  15. 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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