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Genetic Algorithms: Colour Image Segmentation Project Proposal

Keri Woods Marco Gallotta Supervisor: Audrey Mbogho. Genetic Algorithms: Colour Image Segmentation Project Proposal. Image Segmentation. Distinguishing objects Simpler to analyse segmented image. Image Segmentation: Shortfalls. Several current approaches

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Genetic Algorithms: Colour Image Segmentation Project Proposal

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  1. Keri Woods Marco Gallotta Supervisor: Audrey Mbogho Genetic Algorithms:Colour Image Segmentation Project Proposal

  2. Image Segmentation • Distinguishing objects • Simpler to analyse segmented image

  3. Image Segmentation: Shortfalls • Several current approaches • Each only performs well on small subset of images: • Colour • Shading • Noise • Textures

  4. Genetic Algorithms • Mimics biological breeding and mutation • Optimisation technique

  5. Colour IS + GA • Genetic algorithms are widely used in image processing • Investigate genetic algorithms approach to colour image segmentation

  6. Parallelisation • GAs are very slow • Known as good parallelisation candidates • Island approach • Try research an approach to run on a grid

  7. Experimentation • Many existing techniques • No single one covers everything • Select some existing implementations and experiment with them

  8. Experimentation • Research existing GA approaches to image segmentation • From results, design and implement our own GA • Parallelise the GA

  9. Experimentation • Experiment with our GA • Tweak it from results • Compare all implementations • Determine effectiveness of GA

  10. Work Allocation • MARCO: Research general image segmentation methods and GA parallelisation • KERI: Research general use of GAs and GAs for image segmentation • SPLIT: Implement/experiment with non-GA algorithms • Design GA • SPLIT: Implement modules of GA • MARCO: Parallelise the algorithm • KERI: Start experimentation

  11. Timeline

  12. Conclusion • Image segmentation important • Colour information improves segmentation • Uncertainty and large search space suggests well suited to GA • GA parallelise well, deal with larger images • GAs used in colour image segmentation before - effective on limited images • Attempt to broaden the range of images

  13. Any Questions?

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