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Optimization of mean-shift scale parameters on the EGEE grid.

Ting Li1, Sorina Camarasu-Pop, Tristan Glatard

  • 1Université de Lyon, CNRS, INSERM, CREATIS, 69621 Villeurbanne, France.

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Summary
This summary is machine-generated.

This study optimized Mean-Shift (MS) image filtering parameters using extensive grid computing. Gradient ascent proved efficient for optimizing MS parameters, demonstrating grid usability for large-scale experiments.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Science

Background:

  • Mean-Shift (MS) is a vital image filtering technique.
  • Optimizing MS scale parameters is crucial for effective image analysis.
  • Large-scale computational resources are needed for exhaustive parameter optimization.

Purpose of the Study:

  • To optimize the scale parameters of Mean-Shift (MS) image filtering.
  • To evaluate the efficiency of the gradient ascent algorithm for MS parameter optimization.
  • To analyze the performance and usability of grid computing environments for such tasks.

Main Methods:

  • A comprehensive parameter sweep experiment was conducted.
  • The experiment utilized 164 CPU-days of computation on the EGEE grid.
  • Mathematical foundations of Mean-Shift and grid deployment were detailed.

Main Results:

  • The gradient ascent algorithm demonstrated high efficiency in optimizing MS parameters.
  • Key observations were made regarding data transfers, reliability, and task scheduling on the grid.
  • CPU time and overall usability of the grid environment were assessed.

Conclusions:

  • Gradient ascent is an effective method for Mean-Shift parameter optimization.
  • The EGEE grid provides a viable platform for large-scale image processing experiments.
  • Findings offer insights into optimizing distributed computing for scientific tasks.