视差不连续区域和重复纹理区域的误匹配率高一直是影响双目立体匹配测量精度的主要问题,为此,本文提出一种基于多特征融合的立体匹配算法。首先,在代价计算阶段,通过高斯加权法赋予邻域像素点的权值,从而优化绝对差之和(Sum of Absolute...视差不连续区域和重复纹理区域的误匹配率高一直是影响双目立体匹配测量精度的主要问题,为此,本文提出一种基于多特征融合的立体匹配算法。首先,在代价计算阶段,通过高斯加权法赋予邻域像素点的权值,从而优化绝对差之和(Sum of Absolute Differences,SAD)算法的计算精度。接着,基于Census变换改进二进制链码方式,将邻域内像素的平均灰度值与梯度图像的灰度均值相融合,进而建立左右图像对应点的判断依据并优化其编码长度。然后,构建基于十字交叉法与改进的引导滤波器相融合的聚合方法,从而实现视差值再分配,以降低误匹配率。最后,通过赢家通吃(Winner Take All,WTA)算法获取初始视差,并采用左右一致性检测方法及亚像素法提高匹配精度,从而获取最终的视差结果。实验结果表明,在Middlebury数据集的测试中,所提SAD-Census算法的平均非遮挡区域和全部区域的误匹配率为分别为2.67%和5.69%,测量200~900 mm距离的平均误差小于2%;而实际三维测量的最大误差为1.5%。实验结果检验了所提算法的有效性和可靠性。展开更多
: Case studies on Poisson lognormal distribution of species abundance have been rare, especially in forest communities. We propose a numerical method to fit the Poisson lognormal to the species abundance data at an ev...: Case studies on Poisson lognormal distribution of species abundance have been rare, especially in forest communities. We propose a numerical method to fit the Poisson lognormal to the species abundance data at an evergreen mixed forest in the Dinghushan Biosphere Reserve, South China. Plants in the tree, shrub and herb layers in 25 quadrats of 20 m× 20 m, 5 m× 5 m, and 1 m× 1 m were surveyed. Results indicated that: (i) for each layer, the observed species abundance with a similarly small median, mode, and a variance larger than the mean was reverse J-shaped and followed well the zero-truncated Poisson lognormal; (ii) the coefficient of variation, skewness and kurtosis of abundance, and two Poisson lognormal parameters (& and μ) for shrub layer were closer to those for the herb layer than those for the tree layer; and (iii) from the tree to the shrub to the herb layer, the α and the coefficient of variation decreased, whereas diversity increased. We suggest that: (i) the species abundance distributions in the three layers reflects the overall community characteristics; (ii) the Poisson lognormal can describe the species abundance distribution in diverse communities with a few abundant species but many rare species; and (iii) 1/α should be an alternative measure of diversity.展开更多
文摘视差不连续区域和重复纹理区域的误匹配率高一直是影响双目立体匹配测量精度的主要问题,为此,本文提出一种基于多特征融合的立体匹配算法。首先,在代价计算阶段,通过高斯加权法赋予邻域像素点的权值,从而优化绝对差之和(Sum of Absolute Differences,SAD)算法的计算精度。接着,基于Census变换改进二进制链码方式,将邻域内像素的平均灰度值与梯度图像的灰度均值相融合,进而建立左右图像对应点的判断依据并优化其编码长度。然后,构建基于十字交叉法与改进的引导滤波器相融合的聚合方法,从而实现视差值再分配,以降低误匹配率。最后,通过赢家通吃(Winner Take All,WTA)算法获取初始视差,并采用左右一致性检测方法及亚像素法提高匹配精度,从而获取最终的视差结果。实验结果表明,在Middlebury数据集的测试中,所提SAD-Census算法的平均非遮挡区域和全部区域的误匹配率为分别为2.67%和5.69%,测量200~900 mm距离的平均误差小于2%;而实际三维测量的最大误差为1.5%。实验结果检验了所提算法的有效性和可靠性。
基金国家自然科学基金，the Forestry Science and TechnologyResearch Planning of Guangdong Province of China，中国科学院知识创新工程项目
文摘: Case studies on Poisson lognormal distribution of species abundance have been rare, especially in forest communities. We propose a numerical method to fit the Poisson lognormal to the species abundance data at an evergreen mixed forest in the Dinghushan Biosphere Reserve, South China. Plants in the tree, shrub and herb layers in 25 quadrats of 20 m× 20 m, 5 m× 5 m, and 1 m× 1 m were surveyed. Results indicated that: (i) for each layer, the observed species abundance with a similarly small median, mode, and a variance larger than the mean was reverse J-shaped and followed well the zero-truncated Poisson lognormal; (ii) the coefficient of variation, skewness and kurtosis of abundance, and two Poisson lognormal parameters (& and μ) for shrub layer were closer to those for the herb layer than those for the tree layer; and (iii) from the tree to the shrub to the herb layer, the α and the coefficient of variation decreased, whereas diversity increased. We suggest that: (i) the species abundance distributions in the three layers reflects the overall community characteristics; (ii) the Poisson lognormal can describe the species abundance distribution in diverse communities with a few abundant species but many rare species; and (iii) 1/α should be an alternative measure of diversity.
