为增强近红外光谱模型通用性,解决直接正交信号校正算法在光谱处理过程中可能出现过拟合、模型不稳定的现象,提出一种将随机森林与直接正交信号校正算法相结合的模型传递方法(Random Forest-Direct Orthogonal Signal Correction,RF-DO...为增强近红外光谱模型通用性,解决直接正交信号校正算法在光谱处理过程中可能出现过拟合、模型不稳定的现象,提出一种将随机森林与直接正交信号校正算法相结合的模型传递方法(Random Forest-Direct Orthogonal Signal Correction,RF-DOSC)。该方法首先利用随机森林算法进行近红外光谱波长点筛选,然后采用直接正交信号校正方法进行光谱处理并建立回归方程,由PLS计算回归系数求得模型传递矩阵。实验使用三台光谱仪(S,S1,S2)测得的玉米近红外光谱数据集建立传递模型,数据集1(D1)水分、油分、蛋白质、淀粉成分预测标准偏差(SEP)分别为0.1267、0.0982、0.1569和0.4051,数据集2(D2)四种成分的SEP分别为0.1548、0.0819、0.1366和0.3836,均小于传统方法。实验结果表明本文所提模型传递方法能有效消除光谱噪声,减小主仪器和从仪器光谱之间的差异,提高模型的稳定性和准确性,实现不同仪器之间模型的共享。展开更多
为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方...为解决因测量环境及仪器差异而导致的近红外光谱模型通用性较差的不足,提出一种基于小波变换动态时间规整算法的模型传递方法(Wavelet transform combined with dynamic time warping,WDTW),从而实现不同仪器之间模型的共享。首先,该方法将光谱进行小波变换预处理,然后利用动态时间规整算法(Dynamic time warping,DTW)找到近红外光谱波长点之间最优的对应关系并建立回归方程。使用近红外药品光谱数据集和汽油数据集建立传递模型,验证了基于小波变换动态时间规整模型传递方法的有效性。汽油光谱数据集C7、C8、C9和C10成分的预测标准偏差(SEP)分别为0.414 4、0.801 1、1.090 4和1.290 8;药品光谱数据集活性、硬度和重量的SEP分别为2.585 6、0.434 5和2.270 3,均小于传统方法。上述实验结果表明,所建立的模型传递方法能有效消除源机光谱和目标机光谱之间的差异,提高模型的稳定性和准确性,实现模型传递的效果。展开更多
为了解决红外图像对比度低、细节模糊的问题,提出了多尺度卷积结合双区间自适应亮度均衡化的红外图像增强方法.首先采用多尺度卷积对图像进行预处理;然后以最大化类内方差且最小化类间方差作为遗传算法适应度函数求解图像亮暗图层的划...为了解决红外图像对比度低、细节模糊的问题,提出了多尺度卷积结合双区间自适应亮度均衡化的红外图像增强方法.首先采用多尺度卷积对图像进行预处理;然后以最大化类内方差且最小化类间方差作为遗传算法适应度函数求解图像亮暗图层的划分阈值,并采用引入细节信息的双区间直方图进行均衡化,同时通过引入均方差和均值的灰度均匀化方式提高图像亮度;最后,将自适应受限拉普拉斯算子提取的细节图像与亮度提升的图像进行线性加权融合重构出细节边缘清晰、对比度较强的图像.采用不同场景下红外图像和细节丰富的灰度图像进行试验并与传统方法进行对比来验证该方法的有效性.本文方法处理后的图像信息熵(Entropy,En)、熵增强(Enhancement by Entropy,EME)和平均梯度(Average Gradient,AG)最大增幅分别由原来的5.0391、13.4461和7.8450增加到7.1633、90.2525和53.6177,表明该方法具有更好的性能.展开更多
Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the reti...Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.展开更多
文摘为增强近红外光谱模型通用性,解决直接正交信号校正算法在光谱处理过程中可能出现过拟合、模型不稳定的现象,提出一种将随机森林与直接正交信号校正算法相结合的模型传递方法(Random Forest-Direct Orthogonal Signal Correction,RF-DOSC)。该方法首先利用随机森林算法进行近红外光谱波长点筛选,然后采用直接正交信号校正方法进行光谱处理并建立回归方程,由PLS计算回归系数求得模型传递矩阵。实验使用三台光谱仪(S,S1,S2)测得的玉米近红外光谱数据集建立传递模型,数据集1(D1)水分、油分、蛋白质、淀粉成分预测标准偏差(SEP)分别为0.1267、0.0982、0.1569和0.4051,数据集2(D2)四种成分的SEP分别为0.1548、0.0819、0.1366和0.3836,均小于传统方法。实验结果表明本文所提模型传递方法能有效消除光谱噪声,减小主仪器和从仪器光谱之间的差异,提高模型的稳定性和准确性,实现不同仪器之间模型的共享。
文摘为了解决红外图像对比度低、细节模糊的问题,提出了多尺度卷积结合双区间自适应亮度均衡化的红外图像增强方法.首先采用多尺度卷积对图像进行预处理;然后以最大化类内方差且最小化类间方差作为遗传算法适应度函数求解图像亮暗图层的划分阈值,并采用引入细节信息的双区间直方图进行均衡化,同时通过引入均方差和均值的灰度均匀化方式提高图像亮度;最后,将自适应受限拉普拉斯算子提取的细节图像与亮度提升的图像进行线性加权融合重构出细节边缘清晰、对比度较强的图像.采用不同场景下红外图像和细节丰富的灰度图像进行试验并与传统方法进行对比来验证该方法的有效性.本文方法处理后的图像信息熵(Entropy,En)、熵增强(Enhancement by Entropy,EME)和平均梯度(Average Gradient,AG)最大增幅分别由原来的5.0391、13.4461和7.8450增加到7.1633、90.2525和53.6177,表明该方法具有更好的性能.
基金supported by the Open Funds from Guangxi Key Laboratory of Image and Graphic Intelligent Processing under Grant No.GIIP2209the National Natural Science Foundation of China under Grant Nos.62172120 and 62002082the Natural Science Foundation of Guangxi Province of China under Grant Nos.2019GXNSFAA245014 and 2020GXNSFBA238014.
文摘Retinal images play an essential role in the early diagnosis of ophthalmic diseases.Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background.At the same time,automated models struggle to capture representative and discriminative retinal vascular features.To fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network.In addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies.We evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and HRF.Our model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.
