research-article

Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression

Authors:

Lars Vidar Magnusson,

Roland OlssonAuthors Info & Claims

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

Pages 461 - 468

https://doi.org/10.1145/2908812.2908926

Published: 20 July 2016 Publication History

Abstract

In this paper, we employ automatic programming, a relatively unknown evolutionary computation strategy, to improve the non-max suppression step in the popular Canny edge detector. The new version of the algorithm has been tested on a dataset widely used to benchmark edge detection algorithms. The performance has increased by 1.9%, and a pairwise student-t comparison with the original algorithm gives a p-value of 6.45 x 10^-9. We show that the changes to the algorithm have made it better at detecting weak edges, without increasing the computational complexity or changing the overall design.

Previous attempts have been made to improve the filter stage of the Canny algorithm using evolutionary computation, but, to our knowledge, this is the first time it has been used to improve the non-max suppression algorithm.

The fact that we have found a heuristic improvement to the algorithm with significantly better performance on a dedicated test set of natural images suggests that our method should be used as a standard part of image analysis platforms, and that our methodology could be used to improve the performance of image analysis algorithms in general.

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Cited By

Jothiaruna NLeema A(2022)Meta-Analysis to Prognosis Myocardial Infarction Using 12 Lead ECGHigh Performance Computing and Networking10.1007/978-981-16-9885-9_39(473-488)Online publication date: 23-Mar-2022
https://doi.org/10.1007/978-981-16-9885-9_39
Ye CYu ZKang RYousaf KQi CChen KHuang Y(2020)An experimental study of stunned state detection for broiler chickens using an improved convolution neural network algorithmComputers and Electronics in Agriculture10.1016/j.compag.2020.105284170(105284)Online publication date: Mar-2020
https://doi.org/10.1016/j.compag.2020.105284

Index Terms

Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Object detection

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Published In

cover image ACM Conferences

GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016

July 2016

1196 pages

ISBN:9781450342063

DOI:10.1145/2908812

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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New York, NY, United States

Publication History

Published: 20 July 2016

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GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Jothiaruna NLeema A(2022)Meta-Analysis to Prognosis Myocardial Infarction Using 12 Lead ECGHigh Performance Computing and Networking10.1007/978-981-16-9885-9_39(473-488)Online publication date: 23-Mar-2022
https://doi.org/10.1007/978-981-16-9885-9_39
Ye CYu ZKang RYousaf KQi CChen KHuang Y(2020)An experimental study of stunned state detection for broiler chickens using an improved convolution neural network algorithmComputers and Electronics in Agriculture10.1016/j.compag.2020.105284170(105284)Online publication date: Mar-2020
https://doi.org/10.1016/j.compag.2020.105284

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