Genetic Algorithms:
Colour Image Segmentation
Project Proposal
Keri Woods
Marco Gallotta
Supervisor:
Audrey Mbogho
Image Segmentation
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Distinguishing objects
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Simpler to analyse segmented image
Image Segmentation: Shortfalls
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Several current approaches
Each only performs well on small subset of
images:
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Colour
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Shading
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Noise
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Textures
Genetic Algorithms
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Mimics biological breeding and mutation
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Optimisation technique
Colour IS + GA
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Genetic algorithms are widely used in image
processing
Investigate genetic algorithms approach to
colour image segmentation
Parallelisation
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GAs are very slow
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Known as good parallelisation candidates
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Island approach
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Try research an approach to run on a grid
Experimentation
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Many existing techniques
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No single one covers everything
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Select some existing implementations and
experiment with them
Experimentation
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Research existing GA approaches to image
segmentation
From results, design and implement our own
GA
Parallelise the GA
Experimentation
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Experiment with our GA
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Tweak it from results
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Compare all implementations
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Determine effectiveness of GA
Work Allocation
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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
Timeline
Conclusion
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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
Any Questions?