An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ...An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.展开更多
Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving ob...Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.展开更多
针对传统高斯肤色模型在肤色和光照变化较大情况下不能有效提取肤色区域的问题,提出一种改进的高斯肤色模型,并将其应用于人脸检测中。模型参数采用一种自适应更新的参数选择方法,通过对相似度人脸和灰度人脸在对应像素点加权相乘的方式...针对传统高斯肤色模型在肤色和光照变化较大情况下不能有效提取肤色区域的问题,提出一种改进的高斯肤色模型,并将其应用于人脸检测中。模型参数采用一种自适应更新的参数选择方法,通过对相似度人脸和灰度人脸在对应像素点加权相乘的方式,得到将肤色相似度信息和灰度分布信息有效结合的人脸肤色模型,并结合Adaboost算法设计了人脸检测方法。在FERET(facial recognition technology database)、LFW(labeled faces in the wild)、GTFD(Georgia Tech face database)和多人脸图库上的实验结果表明,该模型的肤色提取正确率比传统高斯肤色模型提高了27.1%,提出的人脸检测方法的检测率比Adaboost算法提高了5.5%。展开更多
针对自然条件下光照条件变化给大田油菜图像分割带来的问题,该文研究了油菜图像的高斯HI颜色分割算法,为作物生长发育周期的自动识别提供前期准备。已有统计结果表明,在仅保留绿色作物的图像中,不同色调值的像素数量服从高斯分布。该文...针对自然条件下光照条件变化给大田油菜图像分割带来的问题,该文研究了油菜图像的高斯HI颜色分割算法,为作物生长发育周期的自动识别提供前期准备。已有统计结果表明,在仅保留绿色作物的图像中,不同色调值的像素数量服从高斯分布。该文将去掉背景信息的样本数据从RGB颜色模型转换至HSI颜色模型后,统计各个光强的所有像素对应的色调值,并计算其期望值和方差,依次得出所有强度所对应色调值的期望值和方差,建立出油菜作物色调强度查找表(hue intensity-look up table)。在此基础上,计算每个像素的色调值和期望值之间的差值,若差值小于阈值,则像素被分割为作物,否则为背景。为了在高斯HI颜色分割算法中确定合适的阈值,该研究选取了45幅不同天气状况(晴天、阴天和雨天)不同发育阶段(苗期、三叶期和四叶期)的油菜图像作为样本,探讨阈值的选取与分割结果的关系。结果表明阈值在[2.4,2.6]内分割效果最佳,油菜目标的形状特征完整度最好。为了对图像分割结果进行评价,分别利用高斯HI颜色模型、CIVE(color index of vegetation extraction)、EXG-EXR(excess green-excess red)、EXG(excess green)和VEG(vegetation)算法对15幅不同天气状况的图像进行分割。从视觉效果上来看,高斯HI算法仅需少量样本,即可达到满意分割效果。与其他方法相比,高斯HI颜色分割算法的误分割率(misclassification error,ME)仅为1.8%,相对目标面积误差(relative object area error,RAE)仅为3.6%,均优于其他4种算法的试验结果。在分割结果稳定性上,高斯HI颜色算法表现最好,其ME和RAE值的标准差最低,分别为0.7%和4.5%。试验结果表明,高斯HI颜色算法能取得较好的分割效果,而且对光照条件变化并不敏感,同时,能够充分保留油菜形状特征的完整性,为后期油菜生长发育周期的自动识别提供可靠数据。展开更多
文摘An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
文摘Aiming at the problems that the classical Gaussian mixture model is unable to detect the complete moving object, and is sensitive to the light mutation scenes and so on, an improved algorithm is proposed for moving object detection based on Gaussian mixture model and three-frame difference method. In the process of extracting the moving region, the improved three-frame difference method uses the dynamic segmentation threshold and edge detection technology, and it is first used to solve the problems such as the illumination mutation and the discontinuity of the target edge. Then, a new adaptive selection strategy of the number of Gaussian distributions is introduced to reduce the processing time and improve accuracy of detection. Finally, HSV color space is used to remove shadow regions, and the whole moving object is detected. Experimental results show that the proposed algorithm can detect moving objects in various situations effectively.
文摘针对传统高斯肤色模型在肤色和光照变化较大情况下不能有效提取肤色区域的问题,提出一种改进的高斯肤色模型,并将其应用于人脸检测中。模型参数采用一种自适应更新的参数选择方法,通过对相似度人脸和灰度人脸在对应像素点加权相乘的方式,得到将肤色相似度信息和灰度分布信息有效结合的人脸肤色模型,并结合Adaboost算法设计了人脸检测方法。在FERET(facial recognition technology database)、LFW(labeled faces in the wild)、GTFD(Georgia Tech face database)和多人脸图库上的实验结果表明,该模型的肤色提取正确率比传统高斯肤色模型提高了27.1%,提出的人脸检测方法的检测率比Adaboost算法提高了5.5%。
文摘针对自然条件下光照条件变化给大田油菜图像分割带来的问题,该文研究了油菜图像的高斯HI颜色分割算法,为作物生长发育周期的自动识别提供前期准备。已有统计结果表明,在仅保留绿色作物的图像中,不同色调值的像素数量服从高斯分布。该文将去掉背景信息的样本数据从RGB颜色模型转换至HSI颜色模型后,统计各个光强的所有像素对应的色调值,并计算其期望值和方差,依次得出所有强度所对应色调值的期望值和方差,建立出油菜作物色调强度查找表(hue intensity-look up table)。在此基础上,计算每个像素的色调值和期望值之间的差值,若差值小于阈值,则像素被分割为作物,否则为背景。为了在高斯HI颜色分割算法中确定合适的阈值,该研究选取了45幅不同天气状况(晴天、阴天和雨天)不同发育阶段(苗期、三叶期和四叶期)的油菜图像作为样本,探讨阈值的选取与分割结果的关系。结果表明阈值在[2.4,2.6]内分割效果最佳,油菜目标的形状特征完整度最好。为了对图像分割结果进行评价,分别利用高斯HI颜色模型、CIVE(color index of vegetation extraction)、EXG-EXR(excess green-excess red)、EXG(excess green)和VEG(vegetation)算法对15幅不同天气状况的图像进行分割。从视觉效果上来看,高斯HI算法仅需少量样本,即可达到满意分割效果。与其他方法相比,高斯HI颜色分割算法的误分割率(misclassification error,ME)仅为1.8%,相对目标面积误差(relative object area error,RAE)仅为3.6%,均优于其他4种算法的试验结果。在分割结果稳定性上,高斯HI颜色算法表现最好,其ME和RAE值的标准差最低,分别为0.7%和4.5%。试验结果表明,高斯HI颜色算法能取得较好的分割效果,而且对光照条件变化并不敏感,同时,能够充分保留油菜形状特征的完整性,为后期油菜生长发育周期的自动识别提供可靠数据。
