Browse > Article
http://dx.doi.org/10.3837/tiis.2020.11.008

A multisource image fusion method for multimodal pig-body feature detection  

Zhong, Zhen (College of Information Technology Engineering, Tianjin University of Technology and Education)
Wang, Minjuan (College of Information and Electrical Engineering, China Agricultural University)
Gao, Wanlin (College of Information and Electrical Engineering, China Agricultural University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.11, 2020 , pp. 4395-4412 More about this Journal
Abstract
The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.
Keywords
Nonsubsampled shearlet transform; Gabor filter; modified spatial frequency; pulse coupled neural network; multimodal pig-body feature;
Citations & Related Records
연도 인용수 순위
  • Reference
1 T. Pu and G. Ni, "Contrast-based image fusion using the discrete wavelet transform," Optical Engineering, vol. 39, no. 8, pp. 2075-2082, 2000.   DOI
2 S. Balakrishnan, M. Cacciola, L. Udpa, B. P. Rao, T. Jayakumar and B. Raj, "Development of image fusion methodology using discrete wavelet transform for eddy current images," Ndt & E International, vol. 51, no. 10, pp. 51-57, 2012.   DOI
3 C. Liu, L. Jin, H. Tao, G. Li, Z. Zhuang and Zhang, Y, "Multi-focus image fusion based on spatial frequency in discrete cosine transform domain," IEEE Signal Processing Letters, vol. 22, no. 2, pp. 220-224, 2015.   DOI
4 N. Paramanandham and K. Rajendiran, "Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications," Infrared Physics & Technology, vol. 88, pp. 13-22, 2018.   DOI
5 F. V. Moghadam and H. R. Shahdoosti, "A new multifocus image fusion method using contourlet transform," 2017.
6 W. Kong, "Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform," Infrared Physics & Technology, vol. 63, no. 11, pp. 110-118, 2014.   DOI
7 K. Peter, "Model fitting and robust estimation source code for matlab,".
8 W. G. Wan, Y. Yang, H. J. Lee, "Practical remote sensing image fusion method based on guided filter and improved SML in the NSST domain," Signal, Image and Video Processing, vol. 12, no. 5, pp. 959-966, 2018.   DOI
9 X. Jin, G. Chen, J. Hou, "Multimodal sensor medical image fusion based on nonsubsampled shearlet transform and S-PCNNs in HSV space," Signal Processing, vol. 153, pp. 379-395, 2018.   DOI
10 J. Yang and J. Yang, "Multi-Channel Gabor Filter Design for Finger-Vein Image Enhancement," in Proc. of the fifth Inter. Conf. on Image and Graphics, pp. 87-91, 2009.
11 J. Yang, Y. Shi and J. Yang, "Personal identification based on finger-vein features," Computers in Human Behavior, vol. 27, no. 5, pp. 1565-1570, 2011.   DOI
12 X. Xu, D. Shan, G. Wang and X. Jiang, " Multimodal medical image fusion using pcnn optimized by the qpso algorithm," Applied Soft Computing, vol. 46, pp. 588-595, 2016.   DOI
13 L. Tang, J. Qian, L. Li, J. Hu and X. Wu, "Multimodal medical image fusion based on discrete tchebichef moments and pulse coupled neural network," International Journal of Imaging Systems & Technology, vol. 27, no. 1, pp. 57-65, 2017.   DOI
14 Weiwei Kong, Longjun Zhang, Yang Lei, "Novel fusion method for visible light and infrared images based on nsst-sf-pcnn," Infrared Physics & Technology, vol. 65, no. 7, pp. 103-112, 2014.   DOI
15 M. A. Kashiha, C. Bahr, S. Ott, C. Moons, T. A. Niewold and F. Tuyttens, "Automatic monitoring of pig activity using image analysis," Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2013.
16 D. Stajnko, M. Brus and M. Ho Evar, "Estimation of bull live weight through thermographically measured body dimensions," Computers and Electronics in Agriculture, vol. 61, no. 2, pp. 233-240, 2008.   DOI
17 X. Bai, F. Zhou and B. Xue, "Fusion of infrared and visual images through region extraction by using mult-scale center-surround top-hat transform," Optics Express, vol. 19, no. 9, pp. 8444-8457, 2011.   DOI
18 F. R. Caldara, L. S. Dos Santos, S. T. Machado, M. Moi, de Alencar Nääs, Irenilza, and L. Foppa, "Piglets' surface temperature change at different weights at birth," Asian-Australasian journal of animal sciences, vol. 27, no. 3, pp. 431-438, 2014.   DOI
19 M. Alsaaod, C. Syring, J. Dietrich, M. G. Doherr, T. Gujan and A. Steiner, "A field trial of infrared thermography as a non-invasive diagnostic tool for early detection of digital dermatitis in dairy cows," The Veterinary Journal, vol. 199, no. 2, pp. 281-285, 2014.   DOI
20 K. Kawasue, K. D. Win, K. Yoshida and T. Tokunaga, "Black cattle body shape and temperature measurement using thermography and kinect sensor," Artificial Life and Robotics, vol. 22, pp. 464-470, 2017.   DOI
21 G. Bhatnagar, Q. M. J. Wu and Z. Liu, "A new contrast based multimodal medical image fusion framework," Neurocomputing, vol. 157, pp. 143-152, 2015.   DOI
22 C. Siewert, D. Hoeltig, C. Brauer, "Medial infrared imaging of the porcine thorax for diagnosis of lung pathologies," in Proc. of the 21st Int. Pig Veterinary Society Congress, Vol. II, Vancouver, pp. 663, 2010.
23 W. Ye and H. Xin, "Thermographical quantification of physiological and behavioral responses of group-housed young pigs," Transactions of the ASAE, vol. 43, no. 6, pp. 1843-1851, 2000.   DOI
24 T. S. Kammersgaard, J. Malmkvist, L. J. Pedersen, "Infrared thermography-a non-invasive tool to evaluate thermal status of neonatal pigs based on surface temperature," Animal, vol. 7, no. 12, pp. 2026-2034, 2013.   DOI
25 E. Vakaimalar, K. Mala and B. R. Suresh, "Multifocus image fusion scheme based on discrete cosine transform and spatial frequency," Multimedia Tools and Applications, 78, 17573-17587, 2019.   DOI
26 X. Jin, Q. Jiang, S. Yao, D. Zhou, R. Nie and S. J. Lee, "Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain," Infrared Physics & Technology, vol. 88, pp. 1-12, 2018.   DOI
27 L. I. He, L. Lei, Y. Chao and H. Wei, "An improved fusion algorithm for infrared and visible images based on multi-scale transform," Semiconductor Optoelectronics, vol. 74, pp. 28-37, 2016.
28 L. Wang, B. Li and L. F. Tian, "Eggdd: an explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain," Information Fusion, vol. 19, no. 11, pp. 29-37, 2014.   DOI
29 Q. Zhang and B. L. Guo, "Multifocus image fusion using the nonsubsampled contourlet transform," Signal Processing, vol. 89, no. 7, pp. 1334-1346, 2009.   DOI
30 T. Xiang, L. Yan and R. Gao, "A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking pcnn in nsct domain," Infrared Physics & Technology, vol. 69, pp. 53-61, 2015.   DOI
31 Bai and Xiangzhi, "Infrared and visual image fusion through feature extraction by morphological sequential toggle operator," Infrared Physics & Technology, vol. 71, pp. 77-86, 2015.   DOI
32 Y. Ma, J. Chen, C. Chen, F. Fan and J. Ma, "Infrared and visible image fusion using total variation model," Neurocomputing, vol. 202, pp. 12-19, 2016.   DOI
33 Z. Huang, M. Ding and X. Zhang, "Medical image fusion based on non-subsampled shearlet transform and spiking cortical model," Journal of Medical Imaging & Health Informatics, vol. 7, no. 1, pp. 229-234, 2017.   DOI
34 Y. Huang, D. Bi and D. Wu, "Infrared and visible image fusion based on different constraints in the non-subsampled shearlet transform domain," Sensors, vol. 18, no. 4, pp. 1169, 2018.   DOI
35 G. Yang, C. Ikuta, S. Zhang, Y. Uwate, Y. Nishio and Z. Lu, "A novel image fusion algorithm using an nsct and a pcnn with digital filtering," International Journal of Image & Data Fusion, vol. 9, pp. 82-94, 2018.   DOI
36 B. Cheng, L. Jin and G. Li, "A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive dual-pcnn in nsst domain," Infrared Physics & Technology, vol. 91, pp. 153-163, 2018.   DOI
37 Y. Chen and N. Sang, "Attention-based hierarchical fusion of visible and infrared images," Optik - International Journal for Light and Electron Optics, vol. 126, no. 23, pp. 4243-4248, 2015.   DOI
38 J. Ma, C. Chen, C. Li and J. Huang, "Infrared and visible image fusion via gradient transfer and total variation minimization," Information Fusion, vol. 31, pp. 100-109, 2016.   DOI
39 W. Kong, Y. Lei, M. Ren, "Fusion method for infrared and visible images based on improved quantum theory model," Neurocomputing, vol. 212, pp. 12-21, 2016.   DOI
40 K. Ma, K. Zeng and Z. Wang, "Perceptual quality assessment for multi-exposure image fusion," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3345-3356, 2015.   DOI
41 S. M. Nemalidinne and D. Gupta, "Nonsubsampled contourlet domain visible and infrared image fusion framework for fire detection using pulse coupled neural network and spatial fuzzy clustering," Fire Safety Journal, vol. 101, pp. 84-101, 2018.   DOI