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A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain

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Abstract

The multi-source image fusion has been a hot topic during the recent years because of its higher detection rate. To improve the accuracy of pig-body multi-feature detection, a multi-source image fusion method was adopted in this field. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect shape and temperature feature of pig-body, a novel infrared and visible image fusion method was proposed in non-subsampled contourlet transform (NSCT) domain and named NSCT-GF-IAG. Through this technique, the visible and infrared images were first decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information and coarse-scale detail information, Gabor filter with even-symmetry and improved average gradient (IAG) were employed to fuse low-frequency and high-frequency sub-bands, respectively. Next, the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Finally, the shape feature of pig-body was obtained by automatic threshold segmentation and optimized by morphological processing. Moreover, the highest temperature was extracted based on shape segmentation of pig-body. Experimental results showed that the proposed fusion method for detecting multi-feature was capable of achieving 2.175–5.129% higher average segmentation rate than the prevailing conventional methods. Besides this, the proposed method also improved efficiency in terms of time consumption.

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Acknowledgements

The authors would like to thank their colleagues for their support of this work. The detailed comments from the anonymous reviewers were gratefully acknowledged. This work was supported by the Key Research and Development Project of Shandong Province (Grant No. 2019GNC106091) and the National Key Research and Development Program (Grant No. 2016YFD0200600-2016YFD0200602).

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Authors and Affiliations

  1. Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China

    Zhen Zhong, Wanlin Gao & Minjuan Wang

  2. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Zhen Zhong, Wanlin Gao, Abdul Mateen Khattak & Minjuan Wang

  3. College of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China

    Zhen Zhong

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  1. Zhen Zhong
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  2. Wanlin Gao
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Correspondence to Wanlin Gao or Minjuan Wang.

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Zhong, Z., Gao, W., Khattak, A.M. et al. A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain. Multimed Tools Appl 79, 26225–26244 (2020). https://doi.org/10.1007/s11042-020-09044-9

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  • DOI: https://doi.org/10.1007/s11042-020-09044-9

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