lip recognition software using a kohonen algorithm for image compression
Download
Skip this Video
Download Presentation
Lip-recognition Software using a Kohonen Algorithm for Image Compression

Loading in 2 Seconds...

play fullscreen
1 / 15

Lip-recognition Software using a Kohonen Algorithm for Image Compression - PowerPoint PPT Presentation


  • 136 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Lip-recognition Software using a Kohonen Algorithm for Image Compression' - guido


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
outline

Outline

-Problem and motivation

-Data creation: preprocessing

-Kohonen self organization map (SOM)

-Multi-Layer perceptron

-Final results

-Conclusion

-References

problem

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

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

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

-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

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

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

Kohonen SOM

  • A way to avoid problems:
  • We link the first and the last neurons
kohonen som10

Kohonen SOM

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

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

Multi-Layer perceptron: Result

100% Classification rate is obtained

multi layer perceptron result13

Multi-Layer perceptron: Result

100% Classification rate is obtained

With a 400 iterations training.

conclusion

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

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

ad