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Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform.

Jianfang Cao1, Lichao Chen2, Min Wang2

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This study introduces an Otsu-optimized Canny edge detection algorithm, parallelized using MapReduce on Hadoop. It significantly improves edge detection accuracy and reduces processing time for large image datasets.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Parallel Computing

Background:

  • The Canny operator is a standard for edge detection but struggles with large datasets, leading to decreased performance and excessive runtime.
  • Scaling image analysis tasks presents challenges in processing speed and communication costs for traditional algorithms.

Purpose of the Study:

  • To enhance the runtime and edge detection performance of the Canny operator for big data applications.
  • To develop a parallelized Otsu-optimized Canny edge detection algorithm suitable for distributed computing environments.

Main Methods:

  • Implemented an Otsu-optimized Canny edge detection algorithm.
  • Utilized the MapReduce parallel programming model on the Hadoop platform for distributed processing.
  • Constructed experimental datasets from the Pascal VOC2012 image database.

Main Results:

  • The parallel Otsu-Canny algorithm demonstrated superior edge detection performance compared to traditional methods.
  • Achieved a runtime reduction of approximately 67.2% on a 5-node Hadoop cluster for 60,000 images.
  • The system demonstrated a speedup of approximately 3.4 times for large-scale dataset processing.

Conclusions:

  • The proposed parallel Otsu-Canny algorithm effectively addresses the limitations of the traditional Canny operator for big data.
  • The approach offers significant improvements in both edge detection accuracy and processing efficiency.
  • This method shows clear superiority for large-scale image analysis tasks.