A new faster block-matching algorithm （BMA） by using both search candidate and pixd sulzsamplings is proposed. Firstly a pixd-subsampling approach used in adjustable partial distortion search （APDS） is adjusted to...A new faster block-matching algorithm （BMA） by using both search candidate and pixd sulzsamplings is proposed. Firstly a pixd-subsampling approach used in adjustable partial distortion search （APDS） is adjusted to visit about half points of all search candidates by subsampling them, using a spiral-scanning path with one skip. Two sdected candidates that have minimal and second minimal block distortion measures are obtained. Then a fine-tune step is taken around them to find the best one. Some analyses are given to approve the rationality of the approach of this paper. Experimental results show that, as compared to APDS, the proposed algorithm can enhance the block-matching speed by about 30% while maintaining its MSE performance very close to that of it. And it performs much better than many other BMAs such as TSS, NTSS, UCDBS and NPDS.展开更多
在遮挡目标识别中,目标可能会被其他物体遮挡,导致目标的部分有效特征丢失或变形。目标有效特征的减少,使得单一YOLOv4(You Only Look Once version 4)无法准确识别锚框的初始值,使得模型目标识别困难。为此,引入K-means++算法改进单一Y...在遮挡目标识别中,目标可能会被其他物体遮挡,导致目标的部分有效特征丢失或变形。目标有效特征的减少,使得单一YOLOv4(You Only Look Once version 4)无法准确识别锚框的初始值,使得模型目标识别困难。为此,引入K-means++算法改进单一YOLOv4算法,提出基于改进YOLOv4的遮挡目标识别算法。通过非下采样Contourlet变换划分图像为低频部分和高频部分,分别利用线性增强函数和改进的自适应阈值增强图像,并经由非下采样Contourlet逆变换生成重建图像,对其执行模糊对比度增强。选取YOLOv4作为目标识别基础模型,采用深度可分离卷积替代模型中部分卷积,并替换特征金字塔为递归特征金字塔,提升小目标和遮挡目标的特征学习能力。引入K-means++算法自适应获取锚框,优化锚框初始值,并利用完全交并比和交叉熵构建损失函数,训练改进的YOLOv4并将增强后图像输入其中,实现遮挡目标识别。实验结果表明,所提方法能够有效识别图像目标,且识别结果P-R曲线更理想。展开更多
As a less time-consuming procedure, subsampling technology has been widely used in biological monitoring and assessment programs. It is clear that subsampling counts af fect the value of traditional biodiversity indic...As a less time-consuming procedure, subsampling technology has been widely used in biological monitoring and assessment programs. It is clear that subsampling counts af fect the value of traditional biodiversity indices, but its ef fect on taxonomic distinctness(TD) indices is less well studied. Here, we examined the responses of traditional(species richness, Shannon-Wiener diversity) and TD(average taxonomic distinctness: Δ +, and variation in taxonomic distinctness: Λ +) indices to subsample counts using a random subsampling procedure from 50 to 400 individuals, based on macroinvertebrate datasets from three dif ferent river systems in China. At regional scale, taxa richness asymptotically increased with ?xed-count size; ≥250–300 individuals to express 95% information of the raw data. In contrast, TD indices were less sensitive to the subsampling procedure. At local scale, TD indices were more stable and had less deviation than species richness and Shannon-Wiener index, even at low subsample counts, with ≥100 individuals needed to estimate 95% of the information of the actual Δ + and Λ + in the three river basins. We also found that abundance had a certain ef fect on diversity indices during the subsampling procedure, with dif ferent subsampling counts for species richness and TD indices varying by regions. Therefore, we suggest that TD indices are suitable for biodiversity assessment and environment monitoring. Meanwhile, pilot analyses are necessary when to determine the appropriate subsample counts for bioassessment in a new region or habitat type.展开更多
Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequ...Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise.展开更多
We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method...We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.展开更多
基金This project was supported by the National Natural Science Foundation of China (60272099) .
文摘A new faster block-matching algorithm （BMA） by using both search candidate and pixd sulzsamplings is proposed. Firstly a pixd-subsampling approach used in adjustable partial distortion search （APDS） is adjusted to visit about half points of all search candidates by subsampling them, using a spiral-scanning path with one skip. Two sdected candidates that have minimal and second minimal block distortion measures are obtained. Then a fine-tune step is taken around them to find the best one. Some analyses are given to approve the rationality of the approach of this paper. Experimental results show that, as compared to APDS, the proposed algorithm can enhance the block-matching speed by about 30% while maintaining its MSE performance very close to that of it. And it performs much better than many other BMAs such as TSS, NTSS, UCDBS and NPDS.
文摘在遮挡目标识别中,目标可能会被其他物体遮挡,导致目标的部分有效特征丢失或变形。目标有效特征的减少,使得单一YOLOv4(You Only Look Once version 4)无法准确识别锚框的初始值,使得模型目标识别困难。为此,引入K-means++算法改进单一YOLOv4算法,提出基于改进YOLOv4的遮挡目标识别算法。通过非下采样Contourlet变换划分图像为低频部分和高频部分,分别利用线性增强函数和改进的自适应阈值增强图像,并经由非下采样Contourlet逆变换生成重建图像,对其执行模糊对比度增强。选取YOLOv4作为目标识别基础模型,采用深度可分离卷积替代模型中部分卷积,并替换特征金字塔为递归特征金字塔,提升小目标和遮挡目标的特征学习能力。引入K-means++算法自适应获取锚框,优化锚框初始值,并利用完全交并比和交叉熵构建损失函数,训练改进的YOLOv4并将增强后图像输入其中,实现遮挡目标识别。实验结果表明,所提方法能够有效识别图像目标,且识别结果P-R曲线更理想。
基金Supported by the National Natural Science Foundation of China(Nos.31400469,41571495,31770460)the National Science and Technology Basic Research Program(No.2015FY110400-4)+2 种基金the China Three Gorges Corporation Research Project(No.JGJ/0272015)the Key Program of the Chinese Academy of Sciences(Comprehensive Assessment Technology of River Ecology and Environment for the Water Source Region of "South-toNorth Water Diversion Central Route")the Program for Biodiversity Protection(No.2017HB2096001006)
文摘As a less time-consuming procedure, subsampling technology has been widely used in biological monitoring and assessment programs. It is clear that subsampling counts af fect the value of traditional biodiversity indices, but its ef fect on taxonomic distinctness(TD) indices is less well studied. Here, we examined the responses of traditional(species richness, Shannon-Wiener diversity) and TD(average taxonomic distinctness: Δ +, and variation in taxonomic distinctness: Λ +) indices to subsample counts using a random subsampling procedure from 50 to 400 individuals, based on macroinvertebrate datasets from three dif ferent river systems in China. At regional scale, taxa richness asymptotically increased with ?xed-count size; ≥250–300 individuals to express 95% information of the raw data. In contrast, TD indices were less sensitive to the subsampling procedure. At local scale, TD indices were more stable and had less deviation than species richness and Shannon-Wiener index, even at low subsample counts, with ≥100 individuals needed to estimate 95% of the information of the actual Δ + and Λ + in the three river basins. We also found that abundance had a certain ef fect on diversity indices during the subsampling procedure, with dif ferent subsampling counts for species richness and TD indices varying by regions. Therefore, we suggest that TD indices are suitable for biodiversity assessment and environment monitoring. Meanwhile, pilot analyses are necessary when to determine the appropriate subsample counts for bioassessment in a new region or habitat type.
基金financially supported by the Fundamental Research Funds for the Central Universities(No.201822011)the National Natural Science Foundation of China(No.41674118)the National Science and Technology Major Project(No.2016ZX05027002)
文摘Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise.
文摘We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.
