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- 저자 : Hai ming Ni (검색결과 2 건)
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Applying Multifractal Formalism Combined with Wavelet Transform to Wood Defects Detection
Hai ming Ni,Da wei Qi,Hongbo Mu. 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.4
Nowadays, X-ray computed tomography (CT) wood nondestructive testing technology has been applied to detection of internal defects in log for the purpose of obtaining the optimal wood cutting plan. Multifractal spectrum and wavelet transform are usually used for analyzing, modeling, and extracting different complex features of signals and images. A novel CT image edge detection method which using multifractal spectrum theory combined with wavelet transform is applied in this paper. The new method can be divided into the following main steps: (1) Calculating the wavelet module values of wood defect image. (2) Combining wavelet transform module values with multifractal theory. (3) Calculating the multifractal spectrum from the wavelet transform. (4) Selecting the appropriate threshold to wood defects detection. A large number of experimental results show that the new method to recognize the wood defects is effective.
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Applying Multifractal Spectrum Theory to Fingerprint Features Recognition
Hai Ming Ni,Da Wei Qi,Hongbo Mu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1
Fingerprint features recognition which can be used to distinguish between individuals is an intriguing study with many potential applications. In this paper, a new method for fingerprint recognition based on multifractal spectrum theory was proposed. The recognition process can be divided into the following main steps: (1) Extracting the core point in fingerprint; (2) Fragmenting the fingerprint image to get a subimage with fixed size; (3) Thinning the fingerprint image by using an improved OPTA algorithm; (4) Segmenting the curves in fingerprint image into digital straight segments with normalized straight length threshold; (5) Selecting the appropriate dividing scale to segment the processed fingerprint image; (6) Calculating and analyzing the multifractal spectrum curve - f (a) ; (7) Fitting curve equation and extracting the characteristic parameters of a - f(a); (8) Finally, the parameters matching and fingerprint feature recognition. A large number of experimental results show that our method is effective.
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