An effective edge extraction method using improved local binary pattern for blurry digital radiography images

Bi, Bi; Zeng, Li; Shen, Kuan; Jiang, Haina

doi:10.1016/j.ndteint.2012.10.002

Cited by 18 publications

(3 citation statements)

References 22 publications

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“…In this way, a total of 24 object-based texture features was obtained. Finally, in order to extract more spatial information related to forest structure, wavelet transform [48] and mathematical morphology [49] were used to extract spatial texture features, transformed spectral features and edge features (Table 6). Table 5.…”

Section: Feature Variables Extraction From Hyperspectral Imagerymentioning

confidence: 99%

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

2022

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Forest above-ground biomass (AGB) is an important index to evaluate forest carbon sequestration capacity, which is very important to maintain the stability of forest ecosystems. At present, the wide use of remote sensing technology makes it possible to estimate the large-scale forest AGB accurately and efficiently. Airborne hyperspectral remote sensing data can obtain rich spectral information and spatial structure information on the forest canopy with the characteristics of high spatial and hyperspectral resolution. Airborne LiDAR data can describe the three-dimensional structure characteristics of a forest and provide vertical structure information related to biomass. Based on the characteristics of the two data sources, this study takes Gaofeng forest farm in Nanning, Guangxi, as the study area, Chinese fir, pine tree, eucalyptus and other broadleaved trees as the research object, and constructs the AGB estimation models of different tree species by fusing airborne LiDAR and hyperspectral features. Firstly, spectral features, texture features, vegetation index, wavelet transform features and edge features are extracted from hyperspectral data. Canopy structure features, point cloud structure features, point cloud density features and terrain features are extracted from airborne LiDAR data. Secondly, the random forest (RF) method is used to screen the features of the two sets of data, and the features with the highest importance are selected. Finally, based on the optimal features of the two data sources, the forest AGB model is constructed using the multiple stepwise regression method. The results show that the texture features extracted by wavelet transform can be used for AGB modeling. The AGB of eucalyptus has high correlation with height features derived from airborne LiDAR, the AGB of other broadleaved trees mostly depends on the wavelet transform texture features from airborne hyperspectral data, while the AGB of Chinese fir and pine tree has high correlation with both height features and spectral features. Feature-fusion-based LiDAR and hyperspectral data can greatly improve the accuracy of the AGB models. The accuracy of the optimal AGB models of Chinese fir, pine tree, eucalyptus and other broadleaved trees is 0.78, 0.95, 0.72 and 0.89, respectively. In conclusion, more accurate estimation results can be obtained by combining active and passive remote sensing data for forest AGB estimation, which provides a solution for carbon storage assessment and forest ecosystem assessment.

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Section: Feature Variables Extraction From Hyperspectral Imagerymentioning

confidence: 99%

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

2022

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“…Additionally, in order to satisfy real time requirements in real world applications, LBP has been combined with other operations to increase its efficiency. In [14], by integrating an H function into LBP, an efficient edge extraction method, called H-LBP, was proposed. In particular, a novel LBP-based counting scheme was introduced to differentiate noise from true edges for robust edge extraction.…”

Section: Introductionmentioning

confidence: 99%

Accelerated H-LBP-based edge extraction method for digital radiography

Qiao

Zhao

Jian-ping

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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“…This approach allows for the efficient utilization of processing data when dealing with images of lower resolution. In the proposed approach the texture information is obtained from the Laplacian of Gaussian LoG [3] and structure information is extracted by Regularized Heaviside LBP (RH-LBP) [4]. The LoG operator, which consists of a Laplacian function and a Gaussian smoothing filter, is used to attain texture information.…”

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confidence: 99%

An efficient image focus measure based on perceptual features analysis

Gaidhane

2024

Preprint

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In this paper, a new approach no-reference image quality assessment (NR-IQA) focus measure based on the additivity of Renyi Entropy is proposed. In human and computer vision, image quality must be quantified with human perception. Therefore, perceptual features such as image texture and structure are analyzed. It allows us to achieve a better correlation with the subjective quality assessment. The probability coefficients of images are obtained to extract the human visual system (HVS) features, and significant image details are evaluated. The randomness in the information of the image is observed by considering the additivity of Renyi Entropy. The majority of objective IQA algorithms evaluate the image quality by comparing the original image with the distorted. The presented approach is tested on artificial images by introducing a certain amount of blur without noise and in the presence of noise. The effectuality and performance of the presented method on real-time images show consistent responses under blurred and noisy conditions. Moreover, the proposed method is evaluated with three publicly available data sets such as LIVE, TID2013, and CSIQ. The presented method is compared with various existing techniques in the literature, and it is evident from the experiments that the method has better monotonicity and improved focus measures. The proposed approach achieved better performance metrics such as SROCC, KRCC, and PLCC. The computation time and complexity of the presented approach are reduced due to the logarithmic function.

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An effective edge extraction method using improved local binary pattern for blurry digital radiography images

Cited by 18 publications

References 22 publications

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

Accelerated H-LBP-based edge extraction method for digital radiography

An efficient image focus measure based on perceptual features analysis

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