Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is base...Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.展开更多
A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segme...A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.展开更多
Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variabil...Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.展开更多
为了提高对驾驶员疲劳程度检测的准确性与鲁棒性,提出了一种基于主动形状模型的多个特征融合疲劳检测算法。首先利用简单类Haar特征的级联Adaboost算法快速检测出人脸位置,然后对检测到的人脸进行基于主动形状模型(active shape model,A...为了提高对驾驶员疲劳程度检测的准确性与鲁棒性,提出了一种基于主动形状模型的多个特征融合疲劳检测算法。首先利用简单类Haar特征的级联Adaboost算法快速检测出人脸位置,然后对检测到的人脸进行基于主动形状模型(active shape model,ASM)的特征点定位,利用12个ASM特征标记点,得出眼睛、嘴部和头部的状态参数,再相应地计算出PERCLOS(percentage of eyelid closure over the pupil over time)、AECS(average eye closure speed)、哈欠频率、点头频率等4个疲劳特征,最后利用自适应神经模糊推理系统(adaptive network based fuzzy inference system,ANFIS)判决出驾驶员的3级疲劳程度(清醒、疲劳和严重疲劳)。实验结果表明,本方法对驾驶员疲劳检测准确率达93.3%,具有较高的准确性与鲁棒性。展开更多
针对口形特征点定位的准确性问题,提出一种基于HASM(hierarchical active shape model)的口形轮廓定位方法,采用不等步长、不等角度建模策略和口形聚类策略,构建局部纹理模型作为特征点搜索依据,并利用马氏距离选取最佳定位点．试验结...针对口形特征点定位的准确性问题,提出一种基于HASM(hierarchical active shape model)的口形轮廓定位方法,采用不等步长、不等角度建模策略和口形聚类策略,构建局部纹理模型作为特征点搜索依据,并利用马氏距离选取最佳定位点．试验结果表明,HASM模型的口形特征点定位方法使内唇定位和闭口口形定位的准确率达到90%以上．展开更多
文摘Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.
基金The National Natural Science Foundation of China(No60271033)
文摘A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.
文摘Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.
文摘为了提高对驾驶员疲劳程度检测的准确性与鲁棒性,提出了一种基于主动形状模型的多个特征融合疲劳检测算法。首先利用简单类Haar特征的级联Adaboost算法快速检测出人脸位置,然后对检测到的人脸进行基于主动形状模型(active shape model,ASM)的特征点定位,利用12个ASM特征标记点,得出眼睛、嘴部和头部的状态参数,再相应地计算出PERCLOS(percentage of eyelid closure over the pupil over time)、AECS(average eye closure speed)、哈欠频率、点头频率等4个疲劳特征,最后利用自适应神经模糊推理系统(adaptive network based fuzzy inference system,ANFIS)判决出驾驶员的3级疲劳程度(清醒、疲劳和严重疲劳)。实验结果表明,本方法对驾驶员疲劳检测准确率达93.3%,具有较高的准确性与鲁棒性。
文摘针对口形特征点定位的准确性问题,提出一种基于HASM(hierarchical active shape model)的口形轮廓定位方法,采用不等步长、不等角度建模策略和口形聚类策略,构建局部纹理模型作为特征点搜索依据,并利用马氏距离选取最佳定位点．试验结果表明,HASM模型的口形特征点定位方法使内唇定位和闭口口形定位的准确率达到90%以上．
