TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A multisource image fusion method for multimodal pig-body feature detection |
| 영문제목(English Title) |
A multisource image fusion method for multimodal pig-body feature detection |
| 저자(Author) |
Zhen Zhong
Minjuan Wang
Wanlin Gao
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| 원문수록처(Citation) |
VOL 14 NO. 11 PP. 4395 ~ 4412 (2020. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English 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
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| 키워드(Keyword) |
Nonsubsampled shearlet transform
Gabor filter
modified spatial frequency
pulse coupled neural network
multimodal pig-body feature
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| 파일첨부 |
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