Research on Peak-detection Algorithm for High-precision Demodulation System of Fiber Bragg Grating

Peng Wang,Xu Han,Simin Guan,Hong Zhao,Minglei Shao 보안공학연구지원센터 2014 International Journal of Hybrid Information Techno Vol.7 No.6

In order to improve the detection accuracy of wavelength, the filtering and curve fitting technologies were applied in the FBG wavelength demodulation system based on tunable F-P filter. These methods could realize the accurate peak-location of output signals of the photo detector. According to the characteristics of noise, the FIR low-pass filter was designed to filter the obtained light power signals so as to provide the input signals with high SNR for the peak-detection algorithms. By analyzing and comparing several typical peak-searching algorithms, the algorithm of Gauss formula nonlinear curve fitting (L-M) was chosen to fit the digitized light power signals. The experimental results show that L-M fitting algorithm reduces the mean square error by 7.5% compared with the Gauss fitting algorithm. For the Gauss signal in the wavelength demodulation system designed in the paper, the L-M algorithm has lower mean square error than other peak-searching algorithms. This algorithm is suitable for FBG wavelength demodulation system based on tunable F-P filter. It can efficiently raise the accuracy of wavelength demodulation system.

Development of Cemented Carbide Industry of China

Shao Peng,Fangling Zou,Yonggui Zhou,Huan Wang 한국분말야금학회 2006 한국분말야금학회 학술대회논문집 Vol.2006 No.1

Through the review of developing course of China cemented carbide industry, the writer of this paper at the first time generalizes it into five stages. The writer analyses China cemented carbide industry present status in aspects of produce technology, product structure, operation and management etc. Mean while by analysis of existed shortages in cemented carbide industry, the write considers that it also has three advantages in resource and scale, industry foundation and categories, market and price, and brought out some suggestion and imagination for the future develpment of China cemented carbide industry.

Correlation Tracking with Correcting and Adaptive Update Strategy

Shao-Hu Peng,남현도 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.5

Object tracking plays an important role in the research field of computer vision. Correlation filter (CF) based tracking algorithms have shown remarkable performance recently. However, there are two problems: (1) the online model is prone to drift due to the fixed coefficient update strategy; (2) a tracking error is susceptible to lead the failure of the following tracking task due to the absence of a correcting strategy. To deal with these limitations, we proposed a new correlation tracking filter that includes an adaptive update strategy and a correcting strategy. The adaptive update strategy is based on the confident degree of the tracking result, which can minimize the effect of image noise. And the correcting strategy is based on a four-level classifier that can enhance the error correcting ability. Based on these two strategies, the proposed CCAS not only can improve the accuracy of the correlation tracking, but also can build a detector with strong error correcting ability to handle a variety of challenges. Experiments show the proposed method has the effective correcting ability and can resist model drift. It is noted that the proposed algorithm not only outperforms other state-of-art algorithms but also runs enough fast for real time application.

An Improved Texture Feature Extraction Method for Recognizing Emphysema in CT Images

Shao-Hu Peng,Hyun-Do Nam 한국조명·전기설비학회 2010 조명·전기설비학회논문지 Vol.24 No.11

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

A visual shape descriptor using sectors and shape context of contour lines

Peng, Shao-Hu,Kim, Deok-Hwan,Lee, Seok-Lyong,Chung, Chin-Wan Elsevier 2010 Information sciences Vol.180 No.16

<P><B>Abstract</B></P><P>This paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching. The proposed descriptor consists of two-component feature vectors. First, the local region is separated into sectors and their gradient magnitude and orientation values are extracted; a feature vector is then constructed from these values. Second, local shape features are obtained using the shape context of contour lines. Another feature vector is then constructed from these contour lines. The proposed approach calculates the local shape feature without needing to consider the edges. This can overcome the difficulty associated with textured images and images with ill-defined edges. The combination of two-component feature vectors makes the proposed descriptor more robust to image scale changes, illumination variations and noise. The proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy: 14.525% better than SIFT, 21% better than PCA-SIFT, 11.86% better than GLOH, and 25.66% better than the shape context.</P>

Quantitative Image Analysis of Chest CT Using Gray Level Local Binary Pattern Texture Feature

Shao-Hu Peng,Khairul Muzzammil,Deok-Hwan Kim 한국콘텐츠학회 2009 ICCC International Digital Design Invitation Exhib Vol.2009 No.12

Texture feature is one of the most popular image analysis methods for computer-aided diagnosis (CAD) system. This paper presents a texture feature extraction method based on gray level local binary pattern (GLLBP) to help the diagnosis of emphysema disease using chest CT images. The proposed method allows us to extract texture features with multiple directions. Experimental results show that GLLBP can achieve better performance than the existing texture features.

A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

Shao-Hu Peng,Hyun-Do Nam 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.4

Internal cracks in products are invisible and can lead to fatal crashes or damage. Since Xrays can penetrate materials and be attenuated according to the material’s thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

블럽 검출기를 이용한 X-Ray 영상에서의 BGA 검출기법

Shao-Hu Peng,이혜정,남현도,심재선 대한전기학회 2011 정보 및 제어 심포지엄 논문집 Vol.2011 No.4

A Robust Crack Filter Based on Local Gray Level Variation and Multiscale Analysis for Automatic Crack Detection in X-ray Images

Peng, Shao-Hu,Nam, Hyun-Do The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.4

Internal cracks in products are invisible and can lead to fatal crashes or damage. Since X-rays can penetrate materials and be attenuated according to the material’s thickness and density, they have rapidly become the accepted technology for non-destructive inspection of internal cracks. This paper presents a robust crack filter based on local gray level variation and multiscale analysis for automatic detection of cracks in X-ray images. The proposed filter takes advantage of the image gray level and its local variations to detect cracks in the X-ray image. To overcome the problems of image noise and the non-uniform intensity of the X-ray image, a new method of estimating the local gray level variation is proposed in this paper. In order to detect various sizes of crack, this paper proposes using different neighboring distances to construct an image pyramid for multiscale analysis. By use of local gray level variation and multiscale analysis, the proposed crack filter is able to detect cracks of various sizes in X-ray images while contending with the problems of noise and non-uniform intensity. Experimental results show that the proposed crack filter outperforms the Gaussian model based crack filter and the LBP model based method in terms of detection accuracy, false detection ratio and processing speed.

An Improved Texture Feature Extraction Method for Recognizing Emphysema in CT Images

Peng, Shao-Hu,Nam, Hyun-Do The Korean Institute of IIIuminating and Electrica 2010 조명·전기설비학회논문지 Vol.24 No.11

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.