Flexible edge detection and its enhancement by smell bees optimization algorithm

Abstract

Edge detection still nowadays a complex challenge since the intrinsic proprieties of an image vary from one case to another. Thus, capturing the semantic content of an image relies only on the human interpretation. In this paper, a novel edge detection algorithm is proposed. Compared to usual edge detectors based on derivative filters, the idea behind the proposed algorithm is to extract edges by exploiting only information present in the image itself without need of any extra information. The used detection process is composed of two main phases, smoothing the image and extracting edges. Besides the simplicity of its implementation, the detection algorithm CMAX is doted to more flexibility enabling us to decide on the degree of details embedded in each region of the image independently. Also, as a complementary phase, the quality of detection can be improved by using an optimization approach based on the nature-inspired algorithm smell bees optimization. The quantitative evaluation results of CMAX before and after enhancement and their comparison with others well-known detectors are done by using the benchmark of Berkeley images.

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Notes

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    http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_code.tgz.

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Correspondence to Yamina Mohamed Ben Ali.

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Mohamed Ben Ali, Y. Flexible edge detection and its enhancement by smell bees optimization algorithm. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05769-2

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Keywords

  • Edge detection
  • Smell Bees Optimization
  • Enhancement detection
  • Image processing