Cao, J., Chen, L., Wang, M. and Tian, Y. (2018) Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform. Computational Intelligence and Neuroscience, 2018, Article ID: 3598284.
https://doi.org/10.1155/2018/3598284

has been cited by the following article:

• TITLE: A Distributed Ant Colony Optimization Applied in Edge Detection

  AUTHORS: Min Chen

  KEYWORDS: Distributed System, Ant Colony Optimization, Edge Detection, MapReduce, Speedup

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.8, August 29, 2024

  ABSTRACT: With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.