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GPU Accelerated Edge-Region Based Level Set Evolution Constrained by 2D Gray-scale Histogram

GPU Accelerated Edge-Region Based Level Set Evolution Constrained by 2D Gray-scale Histogram. Paper Review Zhiqiang 9/21/12. Background– Active contour for image segmentation. Active contour and it’s level set implementation Pro: Topological changes are handled naturally.

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GPU Accelerated Edge-Region Based Level Set Evolution Constrained by 2D Gray-scale Histogram

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  1. GPU Accelerated Edge-Region Based Level Set Evolution Constrained by 2D Gray-scale Histogram Paper Review Zhiqiang 9/21/12

  2. Background– Active contour for image segmentation • Active contour and it’s level set implementation • Pro: Topological changes are handled naturally Image to be segmented Level set distance function Initial contour

  3. Background– Active contour for image segmentation CV-MODEL (region based active contour model) Its main idea is to consider the information inside the regions, and not only at their edge. Energy function (or cost function): where u is the distance function. And C is represented as the zero level set of u. Minimizingwith gradient descent flow method

  4. Research Problem -- weakness of region based model Example which success Example which fall

  5. Research Problem -- Advantage of edge based model GAC-MODEL (Edge based active contour model) Original image I g is an edge-stopping function defined as follow: g(I)

  6. Research Problem -- Advantage of edge based model success failure

  7. Claimed Contribution • New model which use simultaneously edge, region and 2D histogram information in order to efficiently segment objects of interest in a given scene • Lattice Boltzmann Method (LBM) is proposed to compute the model in parallel

  8. Edge and region estimate • Edge detector: The diffusivity coefficient g(I) is adapted to the image itself. g(I)is large when is small on intra regions. And g(I)become small when is large near edges.(same with GAC model) • Region detector: Inter-class Variance. (same with CV model)

  9. Speed control • Region selector: Using different evolution speed in various regions based on gradient histogram analysis. • Diffusion equation with a body force:

  10. Experiment results • GPU implementation: Parallel computing toolbox of matlab R2012a and NVIDIA GPU GT 430.(ignore the dates transferring time between CPU and GPU)

  11. Contribution analysis • The proposed model isn’t novel, And segmentation results seem not to be art of the state. • Considering other work that focus on using LBM for active contour model, What’s the unique contribution of this paper?

  12. Algorithm analysis LBM (step 4) may be very fast on GPU, but the computing of image features which involve statistical information would be time consuming. • Which parts of algorithm is computed on GPU? • Computing time for each step?

  13. Questions?

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