A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain
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A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain Zhen Zhong 1,2,3 & Wanlin Gao 1,2 & Abdul Mateen Khattak 2 & Minjuan Wang 1,2 Received: 22 May 2019 / Revised: 12 February 2020 / Accepted: 7 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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 evensymmetry 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. Keywords Nonsubsampled contourlet transform . Gabor filter . Improved average gradient . Pigbody shape segmentation . Pig-body temperature detection
* Wanlin Gao [email protected] * Minjuan Wang [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Nowadays, the universal law of life has been recognized by the study of phenotypic features of common modal animals, such as zebrafish [10, 18], mouse [26], and rat [32] etc. Traditional modal animals have made contributions to the understanding of the elements, such as cells, tissues functions, and the basic mechanisms of life. Therefore, the modal animal phenotypic feature analysis is an effective way to understand human physiology and pathology. However, the life ways of small-scale modal animals are different from human, which do not meet the needs of human major diseases research. So the study on multi-feature representation method of large-scale modal animals has become an important research area. In this regard, the subject of this study encompasses the detection of pig-body shape and temperature feature for breeding purposes.
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