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Conformal monogenic phase congruency model-based edge detection in color images

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Abstract

To enhance the precision of edge localization and noise suppression in a color image, we propose a conformal monogenic phase congruency model-based (CMPCM) edge detection algorithm that has a good analytical capability in a spatial domain for local structural features to exploit points of the maximum phase congruency in two-dimensional images, and employ Pratt’s Figure of Merit (PFOM) evaluation metrics to measure the performance of its edge detection. Comprehensive experiments were conducted on synthetic color images and natural color images from BSDS500 and LPAICI standard image datasets. The experimental results demonstrated that the proposed CMPCM algorithm outperforms other algorithms, such as viz. Canny, LOG, VPMM, PC and MPC, and has smaller computational time consumption as well.

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  1. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

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Acknowledgements

This work was supported by the Special Scientific Research Project of Education Department of Shaanxi Provincial Government (No.16JK1328), and the Natural Science Research Plan in Shaanxi Province of China(Youth Programs, No. 2017JQ6071).

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Authors and Affiliations

  1. Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi’an Polytechnic University, No. 19 Jinhua South Road, 710048, Xi’an, China

    Meihong Shi & Xueqing Zhao

  2. Xi’an Polytechnic University, No. 19 Jinhua South Road, 710048, Xi’an, China

    Dongdong Qiao, Bugao Xu & Chunmei Li

  3. Department of Computer Science and Engineering, University of North Texas, 3940 N.Elm, Room F201, Denton, TX, 76207, USA

    Bugao Xu

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  1. Meihong Shi
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  2. Xueqing Zhao
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  3. Dongdong Qiao
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  5. Chunmei Li
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Correspondence to Meihong Shi.

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Shi, M., Zhao, X., Qiao, D. et al. Conformal monogenic phase congruency model-based edge detection in color images. Multimed Tools Appl 78, 10701–10716 (2019). https://doi.org/10.1007/s11042-018-6617-x

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