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Evolutionary-Automated Image Processing for Computer Vision

Introduction. Computer Vision requires quality image processing techniquesMost image processing techniques require manual adjustments.This is not desirable for an automated computer vision system. Introduction (cont

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Evolutionary-Automated Image Processing for Computer Vision

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    1. Evolutionary-Automated Image Processing for Computer Vision Ty Jones UNR Department of Computer Science

    3. Introduction (cont…) Many current approaches make assumptions: Most images will be similar The same default parameters should be good enough for all images. This is obviously not a very robust approach. Time of day Weather variations Intermittent noise

    4. Our Approach Use Evolutionary Algorithms (EA’s) Able to search vast, complex, non-linear space

    5. Advantages Evolutionary Algorithms give us the power to find the global optimum for input parameters We can let them find the optimum without manual interaction. Using a long-term population, we can converge on the optimum faster over time.

    6. Disadvantages EA’s can be very slow. Which necessitates a long-term population. EA’s can sometimes get “stuck” on a local optimum Developing a good objective function is difficult

    7. Method We wanted to design a system that would optimize any image-processing algorithm that had input parameters Smoothing Sharpening Edge-detection Segmentation Etc…

    8. Method (cont…) Used a previously developed EA library which was very powerful. Picked a candidate algorithm to implement for our design: Phoenix Segmentation Algorithm Seventeen Input parameters Uses histogram data for recursive region-based segmentation

    9. Phoenix Algorithm It is a recursive region-splitting segmentation algorithm Uses histogram information to define regions. Then recursively thresholds regions to define new regions, and so on…

    10. What do the Chromosomes encode? The chromosomes encode the input parameters to the algorithm. Thus, each allele represents one input parameter. Therefore, each chromosome in the population represents one try at segmentation.

    11. Phoenix (Cont…) What do the input parameters do? How high must a histogram peak be to be considered valid? How much area must a peak have? How many peaks can we have in a region? How big must a connected component be? Etc…

    12. Objective Function Essentially, the fitness of a chromosome is computed based on the intersection of the region border pixels and the edges produced by the canny edge-detector.

    13. Conclusion This idea has been implemented with good results by Bhanu. We would like to make improvements upon the objective function. We would also like to expand the idea to make a highly accurate automated system.

    14. References Bir Bhanu, S. Lee, J. Ming . Adaptive Image Segmentation Using a Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Vol.25, No. 12, Dec. 1995, pp.1543-1567 Ron Ohlander, K. Price, D. Raj Reddy. Picture Segmentation Using A Recursive Region Splitting Method. Computer Graphics and Image Processing, Vol. 8, 1978, pp.313-333 Steven Shafer, T. Kanade. Recursive Region Segmentation by Analysis of Histograms. IEEE International Conference on Acoustics, Speech, & Signal Processing, 1982, pp. 1166-1171

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